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		<updated>2026-04-04T16:12:12Z</updated>
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	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=347</id>
		<title>Setup EEG amplifier plugins in MNE Scan</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=347"/>
				<updated>2016-11-11T17:33:19Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: /* Save to a FIFF file */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Available amplifier plugins in MNE Scan ==&lt;br /&gt;
Following driver setup tutorials are offered for MNE Scan:&lt;br /&gt;
&lt;br /&gt;
[[EEGoSports]], &lt;br /&gt;
[[TMSI Refa]],&lt;br /&gt;
[[gUSBAmp]] and &lt;br /&gt;
[[BrainAmp]]&lt;br /&gt;
&lt;br /&gt;
== Save to a FIFF file == &lt;br /&gt;
&lt;br /&gt;
Every acquisition plugin provides the possibility of saving the acquired data stream to a FIFF file. For that purpose two icons can be seen in the status bar of an acquisition plugin:&lt;br /&gt;
&lt;br /&gt;
By clicking the database button, [[File:Database.png|20px|link=]], all storage information of the FIFF file can be set. In addition, an electrode layout file (.elc) can be deposited to the stream. The settings are saved at once and the window can be closed. &lt;br /&gt;
&lt;br /&gt;
[[File:AdjustDatabase.jpg|center|900px|thumb| GUI of the FIFF file storage settings]]&lt;br /&gt;
&lt;br /&gt;
After the acquisition has been started, the record icon, [[File:Record.png|20px|link=]], can be clicked which will initialize the recording. After that it will blink continuously in order to signalize its record status. By clicking on it again, the recording will stop and the FIFF file is saved to the desired location with all its settings.&lt;br /&gt;
&lt;br /&gt;
== Structure of an acquisition plugin ==&lt;br /&gt;
&lt;br /&gt;
The acquisition plugins of MNE Scan have similiar structure and therefore can be explained on the basis of the gUSBamp EEG driver example. The following flow sheet describes the structure of this acquisition plugin.&lt;br /&gt;
&lt;br /&gt;
[[File:Structure_gUSBampAcquisition.png|center|900px|thumb| Flow sheet of the gUSBamp acquisition plugin]]&lt;br /&gt;
&lt;br /&gt;
On the left side the border of MNE Scan can be seen, whereas on the right side the border to the actual device, in this case the gUSBamp amplifier, is depicted. In between, according to their hierarchical order, the three classes are shown as the interface between program and device:&lt;br /&gt;
&lt;br /&gt;
# '''gUSBamp''': regulates the communication between MNE Scan and the driver and acts as the main-class.&lt;br /&gt;
# '''gUSBampproducer''': controls the data acquisition and manages the interface between driver and gUSBamp&lt;br /&gt;
# '''gUSBampdriver''': actual driver which provides data acquisition and controls the communication between the project and the device&lt;br /&gt;
&lt;br /&gt;
However, gUSBampdriver is the actual class, communicating and exchanging data with the device. The gUSBamp and gUSBampproducer classes can be seen as a way, how to integrate the gUSBampdriver class properly into the project.&lt;br /&gt;
&lt;br /&gt;
When establishing a new plugin to the plugin-box like shown in the [[gUSBAmp]] example, all three classes are initialized one after another by calling the constructors. During this process, all default parameter are generated. After that, the main thread returns to the program and the driver plugin is waiting for the start command or further changing instructions of the parameter by the GUI.&lt;br /&gt;
&lt;br /&gt;
By starting the acquisition, one class invokes the next. At the end, the &amp;quot;gUSBampdriver&amp;quot; class initializes the device with the new parameters and sets the device status to &amp;quot;run&amp;quot;. After that, &amp;quot;gUSBamp&amp;quot; and &amp;quot;gUSBampproducer&amp;quot; class are each starting an internal thread which call repetitively  for new data packages from the subordinate class and returning them to the overlying class until the data packages reach the MNE Scan environment. This is achieved with so called ring buffers and leads to a continuous data stream.&lt;br /&gt;
&lt;br /&gt;
When stopping the acquisition, both threads are interrupted by putting the &amp;quot;is_running&amp;quot; parameter to false and the &amp;quot;gUSBampdriver&amp;quot; class puts the device into standby mode.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=346</id>
		<title>Setup EEG amplifier plugins in MNE Scan</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=346"/>
				<updated>2016-11-11T16:01:40Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Available amplifier plugins in MNE Scan ==&lt;br /&gt;
Following driver setup tutorials are offered for MNE Scan:&lt;br /&gt;
&lt;br /&gt;
[[EEGoSports]], &lt;br /&gt;
[[TMSI Refa]],&lt;br /&gt;
[[gUSBAmp]] and &lt;br /&gt;
[[BrainAmp]]&lt;br /&gt;
&lt;br /&gt;
== Save to a FIFF file == &lt;br /&gt;
&lt;br /&gt;
Every acquisition plugin provides the possibility of saving the acquired data stream to a FIFF file. For that purpose two icons can be seen in the status bar of an acquisition plugin:&lt;br /&gt;
&lt;br /&gt;
By clicking the database button, [[File:Database.png|20px|link=]], all storage information of the FIFF file can be set. In addition, an electrode layout file (.elc) can be deposited to the stream. The settings are saved at once and the window can be closed. &lt;br /&gt;
&lt;br /&gt;
[[File:AdjustDatabase.jpg|center|900px|thumb| GUI of the FIFF file storage settings]]&lt;br /&gt;
&lt;br /&gt;
After the acquisition has been started, the record icon, [[File:Record.png|20px|link=]], can be clicked which will initialize the recording. After that it will blink continuously in order to show its record status. By clicking on it again, the recording will stop and the FIFF file is saved to the desired location with all the settings.&lt;br /&gt;
&lt;br /&gt;
== Structure of an acquisition plugin ==&lt;br /&gt;
&lt;br /&gt;
The acquisition plugins of MNE Scan have similiar structure and therefore can be explained on the basis of the gUSBamp EEG driver example. The following flow sheet describes the structure of this acquisition plugin.&lt;br /&gt;
&lt;br /&gt;
[[File:Structure_gUSBampAcquisition.png|center|900px|thumb| Flow sheet of the gUSBamp acquisition plugin]]&lt;br /&gt;
&lt;br /&gt;
On the left side the border of MNE Scan can be seen, whereas on the right side the border to the actual device, in this case the gUSBamp amplifier, is depicted. In between, according to their hierarchical order, the three classes are shown as the interface between program and device:&lt;br /&gt;
&lt;br /&gt;
# '''gUSBamp''': regulates the communication between MNE Scan and the driver and acts as the main-class.&lt;br /&gt;
# '''gUSBampproducer''': controls the data acquisition and manages the interface between driver and gUSBamp&lt;br /&gt;
# '''gUSBampdriver''': actual driver which provides data acquisition and controls the communication between the project and the device&lt;br /&gt;
&lt;br /&gt;
However, gUSBampdriver is the actual class, communicating and exchanging data with the device. The gUSBamp and gUSBampproducer classes can be seen as a way, how to integrate the gUSBampdriver class properly into the project.&lt;br /&gt;
&lt;br /&gt;
When establishing a new plugin to the plugin-box like shown in the [[gUSBAmp]] example, all three classes are initialized one after another by calling the constructors. During this process, all default parameter are generated. After that, the main thread returns to the program and the driver plugin is waiting for the start command or further changing instructions of the parameter by the GUI.&lt;br /&gt;
&lt;br /&gt;
By starting the acquisition, one class invokes the next. At the end, the &amp;quot;gUSBampdriver&amp;quot; class initializes the device with the new parameters and sets the device status to &amp;quot;run&amp;quot;. After that, &amp;quot;gUSBamp&amp;quot; and &amp;quot;gUSBampproducer&amp;quot; class are each starting an internal thread which call repetitively  for new data packages from the subordinate class and returning them to the overlying class until the data packages reach the MNE Scan environment. This is achieved with so called ring buffers and leads to a continuous data stream.&lt;br /&gt;
&lt;br /&gt;
When stopping the acquisition, both threads are interrupted by putting the &amp;quot;is_running&amp;quot; parameter to false and the &amp;quot;gUSBampdriver&amp;quot; class puts the device into standby mode.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=File:AdjustDatabase.jpg&amp;diff=345</id>
		<title>File:AdjustDatabase.jpg</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=File:AdjustDatabase.jpg&amp;diff=345"/>
				<updated>2016-11-11T15:52:26Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: GUI for setting the database of the storage location for an acquired FIFF file&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;GUI for setting the database of the storage location for an acquired FIFF file&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=File:Record.png&amp;diff=344</id>
		<title>File:Record.png</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=File:Record.png&amp;diff=344"/>
				<updated>2016-11-11T15:45:34Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: Record button, for running the recording&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;Record button, for running the recording&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=File:Database.png&amp;diff=343</id>
		<title>File:Database.png</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=File:Database.png&amp;diff=343"/>
				<updated>2016-11-11T15:44:57Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: database icon for adjusting the storage location of the recorded FIFF-file&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;database icon for adjusting the storage location of the recorded FIFF-file&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=BrainAmp&amp;diff=342</id>
		<title>BrainAmp</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=BrainAmp&amp;diff=342"/>
				<updated>2016-11-11T15:31:03Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
This article describes the installation and usage of the BrainAMP EEG driver plugin for the MNE Scan project.&lt;br /&gt;
&lt;br /&gt;
== Building the BrainAMP EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
In order to build the BrainAMP driver, at first, the header file &amp;quot;BrainAmpIoCtl.h&amp;quot; from the BrainAMP SDK has to be included into the mne-cpp project. These files can be found in the device's attached software library. They have to be copied to the BrainAMP driver repository which is to be found under:&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\applications\mne_scan\plugins\brainamp '''&lt;br /&gt;
&lt;br /&gt;
In a second step, the plugin's source code has to be reintegrated into the mne-cpp project. Therefore, in the file :&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\applications\mne_scan\plugins\plugins.pro '''&lt;br /&gt;
&lt;br /&gt;
the plugin's project &amp;quot;brainamp&amp;quot; has to be restored by deleting the &amp;quot;#&amp;quot; in front of the name in the Sensor section. After this is done, the MNE Scan project can be rebuilt.&lt;br /&gt;
&lt;br /&gt;
== Running the BrainAMP EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
For running the BrainAMP EEG plugin, the according driver has to be installed to the operating system. The files can also be found on the device attached software, as well as a documentation which will guide the whole process. After that, the BrainAMP EEG plugin can be used in the MNE Scan environment, like shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
[[File:BrainAMP_GUI.jpg|center| The GUI of the gUSBamp EEG plugin.]]&lt;br /&gt;
&lt;br /&gt;
Delivered sample frequency and block size can be set on the plugin's surface. Please mind the depicted information. After the settings are done, the acquisition can be started with the green &amp;quot;run&amp;quot; button.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=File:BrainAMP_GUI.jpg&amp;diff=341</id>
		<title>File:BrainAMP GUI.jpg</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=File:BrainAMP_GUI.jpg&amp;diff=341"/>
				<updated>2016-11-11T15:23:27Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: the GUI of the BrainAMP aquisition plugin&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;the GUI of the BrainAMP aquisition plugin&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=BrainAmp&amp;diff=340</id>
		<title>BrainAmp</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=BrainAmp&amp;diff=340"/>
				<updated>2016-11-11T15:22:23Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
This article describes the installation and usage of the BrainAMP EEG driver plugin for the MNE Scan project.&lt;br /&gt;
&lt;br /&gt;
== Building the BrainAMP EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
In order to build the BrainAMP driver, at first, the header file &amp;quot;BrainAmpIoCtl.h&amp;quot; from the BrainAMP SDK has to be included into the mne-cpp project. These files can be found in the device's attached software library. They have to be copied to the BrainAMP driver repository which is to be found under:&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\applications\mne_scan\plugins\brainamp '''&lt;br /&gt;
&lt;br /&gt;
In a second step, the plugin's source code has to be reintegrated into the mne-cpp project. Therefore, in the file :&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\applications\mne_scan\plugins\plugins.pro '''&lt;br /&gt;
&lt;br /&gt;
the plugin's project &amp;quot;brainamp&amp;quot; has to be restored by deleting the &amp;quot;#&amp;quot; in front of the name in the Sensor section. After this is done, the MNE Scan project can be rebuilt.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=BrainAmp&amp;diff=339</id>
		<title>BrainAmp</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=BrainAmp&amp;diff=339"/>
				<updated>2016-11-11T15:21:53Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: Created page with &amp;quot;== Abstract ==  This article describes the installation and usage of the BrainAMP EEG driver plugin for the MNE Scan project.  == Building the BrainAMP EEG plugin ==  In order...&amp;quot;&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
This article describes the installation and usage of the BrainAMP EEG driver plugin for the MNE Scan project.&lt;br /&gt;
&lt;br /&gt;
== Building the BrainAMP EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
In order to build the BrainAMP driver, at first, the header file &amp;quot;BrainAmpIoCtl.h&amp;quot; from the BrainAMP SDK has to be included into the mne-cpp project. These files can be found in the device's attached software library. They have to be copied to the BrainAMP driver repository which is to be found under:&lt;br /&gt;
&lt;br /&gt;
 %\mne-cpp\applications\mne_scan\plugins\brainamp&lt;br /&gt;
&lt;br /&gt;
In a second step, the plugin's source code has to be reintegrated into the mne-cpp project. Therefore, in the file :&lt;br /&gt;
&lt;br /&gt;
 %\mne-cpp\applications\mne_scan\plugins\plugins.pro&lt;br /&gt;
&lt;br /&gt;
the plugin's project &amp;quot;brainamp&amp;quot; has to be restored by deleting the &amp;quot;#&amp;quot; in front of the name in the Sensor section. After this is done, the MNE Scan project can be rebuilt.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=GUSBAmp&amp;diff=338</id>
		<title>GUSBAmp</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=GUSBAmp&amp;diff=338"/>
				<updated>2016-11-11T15:05:09Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: /* Building the gUSBamp EEG plugin */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
This article describes the installation and usage of the gUSBamp EEG driver plugin for the MNE Scan project.&lt;br /&gt;
&lt;br /&gt;
== Building the gUSBamp EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
In order to build the gUSBamp driver, at first, the header file &amp;quot;gtec_gUSBamp.h&amp;quot; and the two library files &amp;quot;gUSBamp_x64.lib&amp;quot; and &amp;quot;gUSBamp_x86.lib&amp;quot; from the gUSBamp SDK have to be included into the mne-cpp project. These files can be found in the device's attached software library. They have to be copied to the gUSBamp driver repository which is to be found under:&lt;br /&gt;
&lt;br /&gt;
'''  %\mne-cpp\applications\mne_scan\plugins\gusbamp  '''&lt;br /&gt;
&lt;br /&gt;
In a second step, the plugin's source code has to be reintegrated into the mne-cpp project. Therefore, in the file :&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\applications\mne_scan\plugins\plugins.pro '''&lt;br /&gt;
&lt;br /&gt;
the plugin's project &amp;quot;gusbamp&amp;quot; has to be restored by deleting the &amp;quot;#&amp;quot; in front of the name in the Sensor section. After this is done, the MNE Scan project can be rebuilt.&lt;br /&gt;
&lt;br /&gt;
== Running the gUSBamp EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
For running the gUSBamp EEG plugin, the according driver has to be installed to the operating system. The files can also be found on the device attached software, as well as a documentation which will guide the whole process. After that, the gUSBamp EEG plugin can be used in the MNE Scan environment, like shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
[[File:GUSBampGUI.jpg|center|900px|thumb| The GUI of the gUSBamp EEG plugin.]]&lt;br /&gt;
&lt;br /&gt;
The connected devices and the sample frequency can be set under &amp;quot;set serials&amp;quot; ande &amp;quot;sample rate&amp;quot;. Up to 3 more gUSBamp slaves can be connected to one gUSBamp master. Usually, all channels are selected by default. Under &amp;quot;Single Channel Select&amp;quot;, this can be changed by clicking on the desired selection. After the settings are done, the acquisition can be started with the green &amp;quot;run&amp;quot; button.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=337</id>
		<title>Setup EEG amplifier plugins in MNE Scan</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=337"/>
				<updated>2016-11-11T11:47:31Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Available amplifier plugins in MNE Scan ==&lt;br /&gt;
Following driver setup tutorials are offered for MNE Scan:&lt;br /&gt;
&lt;br /&gt;
[[EEGoSports]], &lt;br /&gt;
[[TMSI Refa]],&lt;br /&gt;
[[gUSBAmp]] and &lt;br /&gt;
[[BrainAmp]]&lt;br /&gt;
&lt;br /&gt;
== Structure of an acquisition plugin ==&lt;br /&gt;
&lt;br /&gt;
The acquisition plugins of MNE Scan have similiar structure and therefore can be explained on the basis of the gUSBamp EEG driver example. The following flow sheet describes the structure of this acquisition plugin.&lt;br /&gt;
&lt;br /&gt;
[[File:Structure_gUSBampAcquisition.png|center|900px|thumb| Flow sheet of the gUSBamp acquisition plugin]]&lt;br /&gt;
&lt;br /&gt;
On the left side the border of MNE Scan can be seen, whereas on the right side the border to the actual device, in this case the gUSBamp amplifier, is depicted. In between, according to their hierarchical order, the three classes are shown as the interface between program and device:&lt;br /&gt;
&lt;br /&gt;
# '''gUSBamp''': regulates the communication between MNE Scan and the driver and acts as the main-class.&lt;br /&gt;
# '''gUSBampproducer''': controls the data acquisition and manages the interface between driver and gUSBamp&lt;br /&gt;
# '''gUSBampdriver''': actual driver which provides data acquisition and controls the communication between the project and the device&lt;br /&gt;
&lt;br /&gt;
However, gUSBampdriver is the actual class, communicating and exchanging data with the device. The gUSBamp and gUSBampproducer classes can be seen as a way, how to integrate the gUSBampdriver class properly into the project.&lt;br /&gt;
&lt;br /&gt;
When establishing a new plugin to the plugin-box like shown in the [[gUSBAmp]] example, all three classes are initialized one after another by calling the constructors. During this process, all default parameter are generated. After that, the main thread returns to the program and the driver plugin is waiting for the start command or further changing instructions of the parameter by the GUI.&lt;br /&gt;
&lt;br /&gt;
By starting the acquisition, one class invokes the next. At the end, the &amp;quot;gUSBampdriver&amp;quot; class initializes the device with the new parameters and sets the device status to &amp;quot;run&amp;quot;. After that, &amp;quot;gUSBamp&amp;quot; and &amp;quot;gUSBampproducer&amp;quot; class are each starting an internal thread which call repetitively  for new data packages from the subordinate class and returning them to the overlying class until the data packages reach the MNE Scan environment. This is achieved with so called ring buffers and leads to a continuous data stream.&lt;br /&gt;
&lt;br /&gt;
When stopping the acquisition, both threads are interrupted by putting the &amp;quot;is_running&amp;quot; parameter to false and the &amp;quot;gUSBampdriver&amp;quot; class puts the device into standby mode.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=336</id>
		<title>Setup EEG amplifier plugins in MNE Scan</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=336"/>
				<updated>2016-11-11T11:38:39Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Available amplifier plugins in MNE Scan ==&lt;br /&gt;
Following driver setup tutorials are offered for MNE Scan:&lt;br /&gt;
&lt;br /&gt;
[[EEGoSports]], &lt;br /&gt;
[[TMSI Refa]],&lt;br /&gt;
[[gUSBAmp]] and &lt;br /&gt;
[[BrainAmp]]&lt;br /&gt;
&lt;br /&gt;
== Structure of an acquisition plugin ==&lt;br /&gt;
&lt;br /&gt;
The acquisition plugins of MNE Scan have similiar structure and therefore can be explained on the basis of the gUSBamp EEG driver example. The following flow sheet describes the structure of this acquisition plugin.&lt;br /&gt;
&lt;br /&gt;
[[File:Structure_gUSBampAcquisition.png|center|900px|thumb| Flow sheet of the gUSBamp acquisition plugin]]&lt;br /&gt;
&lt;br /&gt;
On the left side the border of MNE Scan can be seen, whereas on the right side the border to the actual device, in this case the gUSBamp amplifier, is depicted. In between, according to their hierarchical order, the three classes are shown as the interface between program and device:&lt;br /&gt;
&lt;br /&gt;
# '''gUSBamp''': regulates the communication between MNE Scan and the driver and acts as the main-class.&lt;br /&gt;
# '''gUSBampproducer''': controls the data acquisition and manages the interface between driver and gUSBamp&lt;br /&gt;
# '''gUSBampdriver''': actual driver which provides data acquisition and controls the communication between the project and the device&lt;br /&gt;
&lt;br /&gt;
However, gUSBampdriver is the actual class, communicating and exchanging data with the device. The gUSBamp and gUSBampproducer classes can be seen as a way, how to integrate the gUSBampdriver class properly into the project.&lt;br /&gt;
&lt;br /&gt;
When establishing a new plugin to the plugin-box like shown in the [[gUSBAmp]] example, all three classes are initialized one after another by calling the constructors. During this process, all default parameter are generated. After that, the main thread returns to the program and the driver plugin is waiting for the start command or further changing instructions of the parameter by the GUI.&lt;br /&gt;
&lt;br /&gt;
By starting the acquisition, one class invokes the next. At the end, the &amp;quot;gUSBampdriver&amp;quot; class initializes the device with the new parameters and sets the device status to &amp;quot;run&amp;quot;. After that, &amp;quot;gUSBamp&amp;quot; and &amp;quot;gUSBampproducer&amp;quot; class are each starting an internal thread which call repetitively  for new data packages from the subordinate class and returning them to the overlying class until the data packages reach the MNE Scan environment. This is achieved with so called ring buffers and leads to a continuous data stream.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=334</id>
		<title>Setup EEG amplifier plugins in MNE Scan</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=334"/>
				<updated>2016-11-11T03:10:39Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Available amplifier plugins in MNE Scan ==&lt;br /&gt;
Following driver setup tutorials are offered for MNE Scan:&lt;br /&gt;
&lt;br /&gt;
[[EEGoSports]], &lt;br /&gt;
[[TMSI Refa]],&lt;br /&gt;
[[gUSBAmp]] and &lt;br /&gt;
[[BrainAmp]]&lt;br /&gt;
&lt;br /&gt;
== Structure of an acquisition plugin ==&lt;br /&gt;
&lt;br /&gt;
The acquisition plugins of MNE Scan have similiar structure and therefore can be explained on the basis of the gUSBamp EEG driver example. The following flow sheet describes the structure of the acquisition plugin.&lt;br /&gt;
&lt;br /&gt;
[[File:Structure_gUSBampAcquisition.png|center|900px|thumb| Flow sheet of the gUSBamp acquisition plugin]]&lt;br /&gt;
&lt;br /&gt;
On the left side the border of MNE Scan can be seen, whereas on the right side the border to the actual device, in this case the gUSBamp amplifier, is depicted. In between, according to their hierarchical order, the three main classes are depicted as the interface between program and device:&lt;br /&gt;
&lt;br /&gt;
# '''gUSBamp''': regulates the communication between MNE Scan and the driver and acts as the main-class.&lt;br /&gt;
# '''gUSBampproducer''': controls the data acquisition and manages the interface between driver and gUSBamp&lt;br /&gt;
# '''gUSBampdriver''': actual driver which provides data acquisition and controls the communication between the project and the device&lt;br /&gt;
&lt;br /&gt;
However, gUSBampdriver is the actual class, communicating and exchanging data with the device. The gUSBamp and gUSBampproducer classes can be seen as a way, how to integrate the gUSBampdriver class properly into the project.&lt;br /&gt;
&lt;br /&gt;
When establishing a new plugin to the plugin-box like shown in the [[gUSBAmp]] example, all three classes are initialized one after another. During this process, all default parameter are generated. After that, the main thread returns to the program and the driver plugin is waiting for the start command or further changing instructions of the parameter by the GUI.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=333</id>
		<title>Setup EEG amplifier plugins in MNE Scan</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=333"/>
				<updated>2016-11-11T03:01:46Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Available amplifier plugins in MNE Scan ==&lt;br /&gt;
Following driver setup tutorials are offered for MNE Scan:&lt;br /&gt;
&lt;br /&gt;
[[EEGoSports]], &lt;br /&gt;
[[TMSI Refa]],&lt;br /&gt;
[[gUSBAmp]] and &lt;br /&gt;
[[BrainAmp]]&lt;br /&gt;
&lt;br /&gt;
== Structure of an acquisition plugin ==&lt;br /&gt;
&lt;br /&gt;
The acquisition plugins of MNE Scan have similiar structure and therefore can be explained on the basis of the gUSBamp EEG driver example. The following flow sheet describes the structure of the acquisition plugin.&lt;br /&gt;
&lt;br /&gt;
[[File:Structure_gUSBampAcquisition.png|center|900px|thumb| Flow sheet of the gUSBamp acquisition plugin]]&lt;br /&gt;
&lt;br /&gt;
On the left side the border of MNE Scan can be seen, whereas on the right side the border to the actual device, in this case the gUSBamp amplifier, is depicted. In between, according to their hierarchical order, the three main classes are depicted as the interface between program and device:&lt;br /&gt;
&lt;br /&gt;
# '''gUSBamp''': regulates the communication between MNE Scan and the driver and acts as the main-class.&lt;br /&gt;
# '''gUSBampproducer''': controls the data acquisition and manages the interface between driver and gUSBamp&lt;br /&gt;
# '''gUSBampdriver''': actual driver which provides data acquisition and controls the communication between the project and the device&lt;br /&gt;
&lt;br /&gt;
However, gUSBampdriver is the actual class, communicating and exchanging data with the device. The gUSBamp and gUSBampproducer classes can be seen as a way, how to integrate the gUSBampdriver class properly into the project.&lt;br /&gt;
&lt;br /&gt;
When establishing a new plugin to the plugin-box like shown in the [[gUSBAmp]] example, all three classes are initialized.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=332</id>
		<title>Setup EEG amplifier plugins in MNE Scan</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=332"/>
				<updated>2016-11-11T01:32:18Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: /* Available amplifier plugins in MNE Scan */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Available amplifier plugins in MNE Scan ==&lt;br /&gt;
Following driver setup tutorials are offered for MNE Scan:&lt;br /&gt;
&lt;br /&gt;
[[EEGoSports]], &lt;br /&gt;
[[TMSI Refa]],&lt;br /&gt;
[[gUSBAmp]] and &lt;br /&gt;
[[BrainAmp]]&lt;br /&gt;
&lt;br /&gt;
== Structure of an acquisition plugin ==&lt;br /&gt;
&lt;br /&gt;
The following flow sheet describes the structure of the gUSBamp acquisition plugin and also can be seen as a role model for all other acquisition plugins in MNE Scan. &lt;br /&gt;
&lt;br /&gt;
[[File:Structure_gUSBampAcquisition.png|center|900px|thumb| Flow sheet of the gUSBamp acquisition plugin]]&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=331</id>
		<title>Setup EEG amplifier plugins in MNE Scan</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=331"/>
				<updated>2016-11-10T15:04:27Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: /* Available amplifier plugins in MNE Scan */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Available amplifier plugins in MNE Scan ==&lt;br /&gt;
Following driver tutorials are offered for MNE Scan:&lt;br /&gt;
&lt;br /&gt;
[[EEGoSports]], &lt;br /&gt;
[[TMSI Refa]],&lt;br /&gt;
[[gUSBAmp]] and &lt;br /&gt;
[[BrainAmp]]&lt;br /&gt;
&lt;br /&gt;
== Structure of an acquisition plugin ==&lt;br /&gt;
&lt;br /&gt;
The following flow sheet describes the structure of the gUSBamp acquisition plugin and also can be seen as a role model for all other acquisition plugins in MNE Scan. &lt;br /&gt;
&lt;br /&gt;
[[File:Structure_gUSBampAcquisition.png|center|900px|thumb| Flow sheet of the gUSBamp acquisition plugin]]&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=File:Structure_gUSBampAcquisition.png&amp;diff=330</id>
		<title>File:Structure gUSBampAcquisition.png</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=File:Structure_gUSBampAcquisition.png&amp;diff=330"/>
				<updated>2016-11-10T14:58:17Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: ViktorKL uploaded a new version of File:Structure gUSBampAcquisition.png&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;The flow sheet of the gUSBamp plugin.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=329</id>
		<title>Setup EEG amplifier plugins in MNE Scan</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=329"/>
				<updated>2016-11-10T14:57:35Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Available amplifier plugins in MNE Scan ==&lt;br /&gt;
Following Tutorials are offered for MNE Scan:&lt;br /&gt;
&lt;br /&gt;
[[EEGoSports]], &lt;br /&gt;
[[TMSI Refa]],&lt;br /&gt;
[[gUSBAmp]] and &lt;br /&gt;
[[BrainAmp]]&lt;br /&gt;
&lt;br /&gt;
== Structure of an acquisition plugin ==&lt;br /&gt;
&lt;br /&gt;
The following flow sheet describes the structure of the gUSBamp acquisition plugin and also can be seen as a role model for all other acquisition plugins in MNE Scan. &lt;br /&gt;
&lt;br /&gt;
[[File:Structure_gUSBampAcquisition.png|center|900px|thumb| Flow sheet of the gUSBamp acquisition plugin]]&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=File:Structure_gUSBampAcquisition.png&amp;diff=328</id>
		<title>File:Structure gUSBampAcquisition.png</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=File:Structure_gUSBampAcquisition.png&amp;diff=328"/>
				<updated>2016-11-10T14:34:33Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: ViktorKL uploaded a new version of File:Structure gUSBampAcquisition.png&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;The flow sheet of the gUSBamp plugin.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=File:Ablaufplan_gUSBamp.pdf&amp;diff=327</id>
		<title>File:Ablaufplan gUSBamp.pdf</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=File:Ablaufplan_gUSBamp.pdf&amp;diff=327"/>
				<updated>2016-11-10T14:31:45Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: Flow sheet of the gUSBamp acquisition plugin.&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;Flow sheet of the gUSBamp acquisition plugin.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=File:Structure_gUSBampAcquisition.png&amp;diff=326</id>
		<title>File:Structure gUSBampAcquisition.png</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=File:Structure_gUSBampAcquisition.png&amp;diff=326"/>
				<updated>2016-11-10T14:26:31Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: The flow sheet of the gUSBamp plugin.&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;The flow sheet of the gUSBamp plugin.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=GUSBAmp&amp;diff=325</id>
		<title>GUSBAmp</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=GUSBAmp&amp;diff=325"/>
				<updated>2016-11-10T14:24:29Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: /* Running the gUSBamp EEG plugin */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
This article describes the installation and usage of the gUSBamp EEG driver plugin for the MNE Scan project.&lt;br /&gt;
&lt;br /&gt;
== Building the gUSBamp EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
In order to build the gUSBamp driver, at first, the header file &amp;quot;gtec_gUSBamp.h&amp;quot; and the two library files &amp;quot;gUSBamp_x64.lib&amp;quot; and &amp;quot;gUSBamp_x86.lib&amp;quot; have to be included into the mne-cpp project. These files can be found in the device's attached software library. They have to be copied to the gUSBamp driver repository which is to be found under:&lt;br /&gt;
&lt;br /&gt;
'''  %\mne-cpp\applications\mne_scan\plugins\gusbamp  '''&lt;br /&gt;
&lt;br /&gt;
In a second step, the plugin's source code has to be reintegrated into the mne-cpp project. Therefore, in the file :&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\applications\mne_scan\plugins\plugins.pro '''&lt;br /&gt;
&lt;br /&gt;
the plugin's project &amp;quot;gusbamp&amp;quot; has to be restored by deleting the &amp;quot;#&amp;quot; in front of the name in the Sensor section. After this is done, the MNE Scan project can be rebuilt.&lt;br /&gt;
&lt;br /&gt;
== Running the gUSBamp EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
For running the gUSBamp EEG plugin, the according driver has to be installed to the operating system. The files can also be found on the device attached software, as well as a documentation which will guide the whole process. After that, the gUSBamp EEG plugin can be used in the MNE Scan environment, like shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
[[File:GUSBampGUI.jpg|center|900px|thumb| The GUI of the gUSBamp EEG plugin.]]&lt;br /&gt;
&lt;br /&gt;
The connected devices and the sample frequency can be set under &amp;quot;set serials&amp;quot; ande &amp;quot;sample rate&amp;quot;. Up to 3 more gUSBamp slaves can be connected to one gUSBamp master. Usually, all channels are selected by default. Under &amp;quot;Single Channel Select&amp;quot;, this can be changed by clicking on the desired selection. After the settings are done, the acquisition can be started with the green &amp;quot;run&amp;quot; button.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=324</id>
		<title>Setup EEG amplifier plugins in MNE Scan</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_EEG_amplifier_plugins_in_MNE_Scan&amp;diff=324"/>
				<updated>2016-11-10T14:23:11Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Available amplifier plugins in MNE Scan ==&lt;br /&gt;
Following Tutorials are offered for MNE Scan:&lt;br /&gt;
&lt;br /&gt;
[[EEGoSports]], &lt;br /&gt;
[[TMSI Refa]],&lt;br /&gt;
[[gUSBAmp]] and &lt;br /&gt;
[[BrainAmp]]&lt;br /&gt;
&lt;br /&gt;
== Structure of an acquisition plugin ==&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=GUSBAmp&amp;diff=323</id>
		<title>GUSBAmp</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=GUSBAmp&amp;diff=323"/>
				<updated>2016-11-10T14:20:20Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
This article describes the installation and usage of the gUSBamp EEG driver plugin for the MNE Scan project.&lt;br /&gt;
&lt;br /&gt;
== Building the gUSBamp EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
In order to build the gUSBamp driver, at first, the header file &amp;quot;gtec_gUSBamp.h&amp;quot; and the two library files &amp;quot;gUSBamp_x64.lib&amp;quot; and &amp;quot;gUSBamp_x86.lib&amp;quot; have to be included into the mne-cpp project. These files can be found in the device's attached software library. They have to be copied to the gUSBamp driver repository which is to be found under:&lt;br /&gt;
&lt;br /&gt;
'''  %\mne-cpp\applications\mne_scan\plugins\gusbamp  '''&lt;br /&gt;
&lt;br /&gt;
In a second step, the plugin's source code has to be reintegrated into the mne-cpp project. Therefore, in the file :&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\applications\mne_scan\plugins\plugins.pro '''&lt;br /&gt;
&lt;br /&gt;
the plugin's project &amp;quot;gusbamp&amp;quot; has to be restored by deleting the &amp;quot;#&amp;quot; in front of the name in the Sensor section. After this is done, the MNE Scan project can be rebuilt.&lt;br /&gt;
&lt;br /&gt;
== Running the gUSBamp EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
For running the gUSBamp EEG plugin, the according driver has to be installed to the operating system. The files can also be found on the device attached software, as well as a documentation which will guide the whole process. After that, the gUSBamp EEG plugin can be used in the MNE Scan environment, like shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
[[File:GUSBampGUI.jpg|center|900px|thumb| The GUI of the gUSBamp EEG plugin.]]&lt;br /&gt;
&lt;br /&gt;
The connected devices and the sample frequency can be set under &amp;quot;set serials&amp;quot; ande &amp;quot;sample rate&amp;quot;. Usually, all channels are selected by default. Under &amp;quot;Single Channel Select&amp;quot;, this can be changed by clicking on the desired selection. After the settings are done, the acquisition can be started with the green &amp;quot;run&amp;quot; button.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=File:GUSBampGUI.jpg&amp;diff=322</id>
		<title>File:GUSBampGUI.jpg</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=File:GUSBampGUI.jpg&amp;diff=322"/>
				<updated>2016-11-10T13:22:01Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: The GUI of the gUSBamp EEG plugin.&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;The GUI of the gUSBamp EEG plugin.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=GUSBAmp&amp;diff=321</id>
		<title>GUSBAmp</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=GUSBAmp&amp;diff=321"/>
				<updated>2016-11-10T12:30:13Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
This article describes the installation and usage of the gUSBamp EEG driver plugin for the MNE Scan project.&lt;br /&gt;
&lt;br /&gt;
== Building the gUSBamp EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
In order to build the gUSBamp driver, at first, the header file &amp;quot;gtec_gUSBamp.h&amp;quot; and the two library files &amp;quot;gUSBamp_x64.lib&amp;quot; and &amp;quot;gUSBamp_x86.lib&amp;quot; have to be included into the gUSBamp driver repository:&lt;br /&gt;
&lt;br /&gt;
'''  %\mne-cpp\applications\mne_scan\plugins\gusbamp  '''&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=EEGoSports&amp;diff=320</id>
		<title>EEGoSports</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=EEGoSports&amp;diff=320"/>
				<updated>2016-11-10T12:11:08Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: /* Abstract */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
This article describes the installation and usage of the EEGoSports EEG driver plugin for the MNE Scan project.&lt;br /&gt;
&lt;br /&gt;
== Building the EEGoSports EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
In order to build the EEGoSports driver plugin, the &amp;quot;eemagine&amp;quot; folder from the SDK has to be added to the EEGoSports folder in the mne-cpp repository in a first step. This location can be found in the mne-cpp repository under:&lt;br /&gt;
&lt;br /&gt;
'''%\mne-cpp\applications\mne_scan\plugins\eegosports\'''&lt;br /&gt;
&lt;br /&gt;
The &amp;quot;eemagine&amp;quot; folder includes another folder called &amp;quot;sdk&amp;quot; which holds the necessary header and source files like: amplifier.h, buffer.h, channel.h, exceptions.h, factory.h, stream.h, version.h, wrapper.cpp and wrapper.h.&lt;br /&gt;
&lt;br /&gt;
In a second step, the eegosports subproject has to be reintegrated into the mne-cpp project. This can be achieved by removing its comment status in the plugins' project file. This file can be found under following path:&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\applications\mne_scan\plugins.pro '''&lt;br /&gt;
&lt;br /&gt;
In order to reintegrate the eegosports subproject into mne-cpp, its comment status has to be removed in this file. This can be achieved by removing the # sign in front of the eegosports project in the Sensors category.&lt;br /&gt;
&lt;br /&gt;
Now, the whole mne-cpp can be rebuilt as normal. MNE Scan then will contain the EEGoSports driver plugin in its Sensor plugin section.&lt;br /&gt;
&lt;br /&gt;
== Running the EEGoSports EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
For a correct recognition of the EEGoSports device, the operating system compatible driver has to be installed with the delivered software. Furthermore the two files &amp;quot;eego-SDK.lib&amp;quot; and &amp;quot;eego-SDK.dll&amp;quot; from the driver's library have to be copied to the location:&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\bin  '''&lt;br /&gt;
&lt;br /&gt;
In a last step, the license files which are delivered with the device have to be copied to&lt;br /&gt;
&lt;br /&gt;
''' C:\Users\''Username''\Documents\Eego '''.&lt;br /&gt;
&lt;br /&gt;
The license files should be named like &amp;quot;EE225-020032-000001&amp;quot;.&lt;br /&gt;
&lt;br /&gt;
After that, the device can be connected to the computer and be switched on and MNE Scan can be started. It is now possible to use the EEGoSports Driver plugin in the MNE Scan environment. By drag&amp;amp;drop, the plugin can be added to the plugin box and connected to other processing items like shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
[[File:EEGoSportsGUI.jpg|center|900px|thumb| GUI of the EEGoSports EEG plugin]]&lt;br /&gt;
&lt;br /&gt;
The GUI of the EEGoSports EEG plugin allows the adjustment of sample frequency and block size. A FIFF-data-stream is now given to every connected processing plugin.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=GUSBAmp&amp;diff=319</id>
		<title>GUSBAmp</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=GUSBAmp&amp;diff=319"/>
				<updated>2016-11-09T16:29:20Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: Created page with &amp;quot;== Abstract ==  This article describes the installation of an gUSBamp device to the MNE Scan environment as well as the usage.&amp;quot;&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
This article describes the installation of an gUSBamp device to the MNE Scan environment as well as the usage.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=318</id>
		<title>Setup SSVEP BCI example</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=318"/>
				<updated>2016-11-09T16:21:46Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: /* Adjusting the Stimulation */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
The SSVEP-BCI plugin beholds a Brain Computer Interface (BCI) using Steady-State-Visual-Evoked-Potentials (SSVEP). It was developed under the Master Thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot;, in which the so called [http://iopscience.iop.org/article/10.1088/1741-2560/8/3/036020/meta Bremen-BCI] was used as a role model in order to setup classification pipeline and visualization. This tutorial describes both the setup of the processing pipeline and usage of the SSVEP-BCI plugin.&lt;br /&gt;
&lt;br /&gt;
== Editing the Processing Pipeline ==&lt;br /&gt;
&lt;br /&gt;
In the following figure an edited setup of the SSVEP-BCI processing pipeline is shown in the MNE Scan GUI environment. For this example the NoiseReduction plugin was interposed as a preprocessing step in order to increase the Signal-to-Noise ratio. However, the SSVEP-BCI plugin can also be processed with &amp;quot;raw&amp;quot; (unfiltered) data by connecting it directly with the  acquisition plugin.&lt;br /&gt;
&lt;br /&gt;
[[File:MNE-Scan processing pipelilne.jpeg|frame|center|SSVEP BCI processing pipeline in the MNE Scan environment]]&lt;br /&gt;
&lt;br /&gt;
== Adjusting the SSVEP-BCI ==&lt;br /&gt;
&lt;br /&gt;
Like already mentioned, in the thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot; different scientific approaches are combined in order to realize this BCI. The adjustment of these features can be done by clicking the configuration icon:[[File:Configuration.png|20px|link=]] on the SSVEP-BCI's GUI which is depictured in the previous figure. With this command, the configuration GUI is opened which is shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
[[File:SSVEPConfigurationWindow.jpg|center|900px|thumb| Configuration Window of the SSVEP-BCI plugin]]&lt;br /&gt;
&lt;br /&gt;
The adjustments can be done online during the processing of the plugin with the configuration GUI:&lt;br /&gt;
&lt;br /&gt;
===== Channel Select =====&lt;br /&gt;
The channel select is realized with a .txt-file which is located under &amp;quot;''MNE-CPP Folder''-bin-mne_scan_plugins-resources-ssvepBCI&amp;quot;, like the already included &amp;quot;Pinning-Scheme-Duke-Dry-64.txt&amp;quot; file. These files behold the mapping of the channel names according to the channel number in the FIFF-file. You can change the current selected folder or link the plugin to a new edited file.&lt;br /&gt;
In the latter case, the new edited file-name has to be changed in line 241 of the &amp;quot;ssvepbciconfiguration.cpp&amp;quot; file. Also the default channels can be changed in the ssvepbci.cpp file in line 104 and 106.&lt;br /&gt;
Now the channel can be selected or deleted by drag&amp;amp;drop between the available- and delete-section&lt;br /&gt;
&lt;br /&gt;
===== Feature Extraction =====&lt;br /&gt;
&lt;br /&gt;
The feature extraction can be realized with either the MEC or the CCA algorithm. Both approaches are described in the corresponding [http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization thesis]. Also the number of generated harmonics of the reference signal can be set in this frame.&lt;br /&gt;
&lt;br /&gt;
===== Classification =====&lt;br /&gt;
&lt;br /&gt;
The state of classification is displayed with five different progress bars belonging each to a different SSVEP frequency. The thresholds can be set with the double-spin-boxes by moving the slider (threshold) up and down. &lt;br /&gt;
&lt;br /&gt;
For a classification indeed to be confirmed, the classification has to be done several times in a sliding list. Following parameter can be set for this process:&lt;br /&gt;
* '''Number of Classification Hits''': is the minimal number of classification hits until there is a confirmation.&lt;br /&gt;
* '''Size of Classification''': is the size of possible entries in to the classification list. By logic, this value is always bigger or equal than the &amp;quot;Number of Classification Hits&amp;quot;.&lt;br /&gt;
* '''Number of Classification Breaks''': Since there are a lot of subject dependent variations, the algorithm needs to be paused between two classifications. Therefore a number of classification breaks can be set with this parameter.&lt;br /&gt;
If a classification has been confirmed, its name will be displayed red and also can be seen in the LCD-number field.&lt;br /&gt;
&lt;br /&gt;
===== Accuracy Feature =====&lt;br /&gt;
&lt;br /&gt;
In order to determine accuracy and robustness of the plugin, a measurement can be started with the screen keyboard device. For this purpose a Measurement can be started and stopped in the corresponding frame. However, the screen keyboard has to be set before which is explained in the following section.&lt;br /&gt;
&lt;br /&gt;
== Adjusting the Stimulation ==&lt;br /&gt;
&lt;br /&gt;
For stimulation, a second screen hast to be attached to the control computer. Performance issues in the visualization of the stimulation are mostly the consequence of old graphic drivers. Since challenging graphical tasks are performed, the latest graphic driver should be installed on the operating system. &lt;br /&gt;
&lt;br /&gt;
The BCI then will adapt automatically to the adjusted frequencies. The configuration of the stimulation can be set by clicking the stimulation icon: [[File:Stimulus.png|20px|link=]]. Consequently, the window for setting the stimulation opens.&lt;br /&gt;
&lt;br /&gt;
[[File:StimulationFeature.jpg|center|900px|thumb| Stimulation feature of the SSVEP-BCI plugin.]]&lt;br /&gt;
&lt;br /&gt;
Three tests can be started by clicking the associated buttons. Every test beholds different numbers of flickering panels with a different size. Each panel can be selected with the first combo box. With the second combo box, the frequency of the selected panel can be adjusted. Below of the combo boxes, the refresh rate of the subject's screen is displayed. If the stimulation screen is shown on the wrong monitor, the screens' numbers have to be exchanged in the main settings of the operating system.&lt;br /&gt;
&lt;br /&gt;
The fourth button initializes the visualization of the screen keyboard device. In the group box &amp;quot;BCI Speller&amp;quot; a short phrase can be set in order to show to the subject in reinitialize the accuracy feature. In this example the chosen phrase is &amp;quot;TU Ilmenau&amp;quot;. By clicking the button &amp;quot;start measurement&amp;quot; in the SSVEP-BCI configuration window. A measurement can be started then.&lt;br /&gt;
&lt;br /&gt;
== Handling of the Screen Keyboard ==&lt;br /&gt;
&lt;br /&gt;
The Screen Keyboard is a special application for SSVEP-BCIs which was designed for the [http://iopscience.iop.org/article/10.1088/1741-2560/8/3/036020/meta Bremen-BCI]. Following figure shows the surface of the subject's screen. &lt;br /&gt;
&lt;br /&gt;
[[File:ScreenKeyboard.jpg|center|900px|thumb| Stimulation feature of the SSVEP-BCI plugin.]]&lt;br /&gt;
&lt;br /&gt;
The subject can control the Screen Keyboard by concentrating of the four direction- and the &amp;quot;Select&amp;quot; panel. The BCI recognizes the selected command and translates them to the red cursor in the middle of the screen. The bottom left corner shows the phrase which is supposed to be spelled. This phrase can be set with the stimulation feature. The accuracy feature then will count the amount of right and wrong commands.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=EEGoSports&amp;diff=317</id>
		<title>EEGoSports</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=EEGoSports&amp;diff=317"/>
				<updated>2016-11-07T13:07:15Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
This article describes the installation and usage of the EEGoSports SDK for the MNE Scan project.&lt;br /&gt;
&lt;br /&gt;
== Building the EEGoSports EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
In order to build the EEGoSports driver plugin, the &amp;quot;eemagine&amp;quot; folder from the SDK has to be added to the EEGoSports folder in the mne-cpp repository in a first step. This location can be found in the mne-cpp repository under:&lt;br /&gt;
&lt;br /&gt;
'''%\mne-cpp\applications\mne_scan\plugins\eegosports\'''&lt;br /&gt;
&lt;br /&gt;
The &amp;quot;eemagine&amp;quot; folder includes another folder called &amp;quot;sdk&amp;quot; which holds the necessary header and source files like: amplifier.h, buffer.h, channel.h, exceptions.h, factory.h, stream.h, version.h, wrapper.cpp and wrapper.h.&lt;br /&gt;
&lt;br /&gt;
In a second step, the eegosports subproject has to be reintegrated into the mne-cpp project. This can be achieved by removing its comment status in the plugins' project file. This file can be found under following path:&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\applications\mne_scan\plugins.pro '''&lt;br /&gt;
&lt;br /&gt;
In order to reintegrate the eegosports subproject into mne-cpp, its comment status has to be removed in this file. This can be achieved by removing the # sign in front of the eegosports project in the Sensors category.&lt;br /&gt;
&lt;br /&gt;
Now, the whole mne-cpp can be rebuilt as normal. MNE Scan then will contain the EEGoSports driver plugin in its Sensor plugin section.&lt;br /&gt;
&lt;br /&gt;
== Running the EEGoSports EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
For a correct recognition of the EEGoSports device, the operating system compatible driver has to be installed with the delivered software. Furthermore the two files &amp;quot;eego-SDK.lib&amp;quot; and &amp;quot;eego-SDK.dll&amp;quot; from the driver's library have to be copied to the location:&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\bin  '''&lt;br /&gt;
&lt;br /&gt;
In a last step, the license files which are delivered with the device have to be copied to&lt;br /&gt;
&lt;br /&gt;
''' C:\Users\''Username''\Documents\Eego '''.&lt;br /&gt;
&lt;br /&gt;
The license files should be named like &amp;quot;EE225-020032-000001&amp;quot;.&lt;br /&gt;
&lt;br /&gt;
After that, the device can be connected to the computer and be switched on and MNE Scan can be started. It is now possible to use the EEGoSports Driver plugin in the MNE Scan environment. By drag&amp;amp;drop, the plugin can be added to the plugin box and connected to other processing items like shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
[[File:EEGoSportsGUI.jpg|center|900px|thumb| GUI of the EEGoSports EEG plugin]]&lt;br /&gt;
&lt;br /&gt;
The GUI of the EEGoSports EEG plugin allows the adjustment of sample frequency and block size. A FIFF-data-stream is now given to every connected processing plugin.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=EEGoSports&amp;diff=316</id>
		<title>EEGoSports</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=EEGoSports&amp;diff=316"/>
				<updated>2016-11-07T12:34:04Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
This article describes the installation and usage of the EEGoSports SDK for the MNE Scan project.&lt;br /&gt;
&lt;br /&gt;
== Building the EEGoSports EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
In order to build the EEGoSports driver plugin, the &amp;quot;eemagine&amp;quot; folder from the SDK has to be added to the EEGoSports folder in the mne-cpp repository in a first step. This location can be found in the mne-cpp repository under:&lt;br /&gt;
&lt;br /&gt;
'''%\mne-cpp\applications\mne_scan\plugins\eegosports\'''&lt;br /&gt;
&lt;br /&gt;
The &amp;quot;eemagine&amp;quot; folder includes another folder called &amp;quot;sdk&amp;quot; which holds the necessary header and source files like: amplifier.h, buffer.h, channel.h, exceptions.h, factory.h, stream.h, version.h, wrapper.cpp and wrapper.h.&lt;br /&gt;
&lt;br /&gt;
In a second step, the eegosports subproject has to be reintegrated into the mne-cpp project. This can be achieved by removing its comment status in the plugins' project file. This file can be found under following path:&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\applications\mne_scan\plugins.pro '''&lt;br /&gt;
&lt;br /&gt;
In order to reintegrate the eegosports subproject into mne-cpp, its comment status has to be removed in this file. This can be achieved by removing the # sign in front of the eegosports project in the Sensors category.&lt;br /&gt;
&lt;br /&gt;
Now, the whole mne-cpp can be rebuilt as normal. MNE Scan then will contain the EEGoSports driver plugin in its Sensor plugin section.&lt;br /&gt;
&lt;br /&gt;
== Running the EEGoSports EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
For a correct recognition of the EEGoSports device, the operating system compatible driver has to be installed with the delivered software. Furthermore the two files &amp;quot;eego-SDK.lib&amp;quot; and &amp;quot;eego-SDK.dll&amp;quot; from the driver's library have to be copied to the location:&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\bin  '''&lt;br /&gt;
&lt;br /&gt;
In a last step, the license files which are delivered with the device have to be copied to&lt;br /&gt;
&lt;br /&gt;
''' C:\Users\''Username''\Documents\Eego '''.&lt;br /&gt;
&lt;br /&gt;
The license files should be named like &amp;quot;EE225-020032-000001&amp;quot;.&lt;br /&gt;
&lt;br /&gt;
After that, the device can be connected to the computer and be switched on and MNE Scan can be started. It is now possible to use the EEGoSports Driver plugin in the MNE Scan environment. By drag&amp;amp;drop the plugin can be added to the plugin box and connected to other processing items like shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
[[File:EEGoSportsGUI.jpg|center|900px|thumb| GUI of the EEGoSports EEG plugin]]&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=EEGoSports&amp;diff=315</id>
		<title>EEGoSports</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=EEGoSports&amp;diff=315"/>
				<updated>2016-11-07T12:33:04Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: /* Abstract */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
This article describes the installation and usage of the EEGoSports SDK for the MNE Scan project.&lt;br /&gt;
&lt;br /&gt;
== Building the EEGoSports EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
In order to build the EEGoSports driver plugin, the &amp;quot;eemagine&amp;quot; folder from the SDK has to be added to the EEGoSports folder in the mne-cpp repository in a first step. This location can be found in the mne-cpp repository under:&lt;br /&gt;
&lt;br /&gt;
'''%\mne-cpp\applications\mne_scan\plugins\eegosports\'''&lt;br /&gt;
&lt;br /&gt;
The &amp;quot;eemagine&amp;quot; folder includes another folder called &amp;quot;sdk&amp;quot; which holds the necessary header and source files like: amplifier.h, buffer.h, channel.h, exceptions.h, factory.h, stream.h, version.h, wrapper.cpp and wrapper.h.&lt;br /&gt;
&lt;br /&gt;
In a second step, the eegosports subproject has to be reintegrated into the mne-cpp project. This can be achieved by removing its comment status in the plugins' project file. This file can be found under following path:&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\applications\mne_scan\plugins.pro '''&lt;br /&gt;
&lt;br /&gt;
In order to reintegrate the eegosports subproject into mne-cpp, its comment status has to be removed in this file. This can be achieved by removing the # sign in front of the eegosports project in the Sensors category.&lt;br /&gt;
&lt;br /&gt;
Now, the whole mne-cpp can be rebuilt as normal. MNE Scan then will contain the EEGoSports driver plugin in its Sensor plugin section.&lt;br /&gt;
&lt;br /&gt;
== Running the EEGoSports EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
For a correct recognition of the EEGoSports device, the operating system compatible driver has to be installed with the delivered software. Furthermore the two files &amp;quot;eego-SDK.lib&amp;quot; and &amp;quot;eego-SDK.dll&amp;quot; from the driver's library have to be copied to the location:&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\bin  '''&lt;br /&gt;
&lt;br /&gt;
In a last step, the license files which are delivered with the device have to be copied to&lt;br /&gt;
&lt;br /&gt;
''' C:\Users\''Username''\Documents\Eego '''.&lt;br /&gt;
&lt;br /&gt;
The license files should be named like &amp;quot;EE225-020032-000001&amp;quot;.&lt;br /&gt;
&lt;br /&gt;
After that, the device can be connected to the computer and be switched on and MNE Scan can be started. It is now possible to use the EEGoSports Driver plugin in the MNE Scan environment. By drag&amp;amp;drop the plugin can be added to the plugin box and connected to other processing items like shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
[[File:EEGoSportsGUI.jpg|center|900px|thumb| Configuration Window of the SSVEP-BCI plugin]]&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=EEGoSports&amp;diff=314</id>
		<title>EEGoSports</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=EEGoSports&amp;diff=314"/>
				<updated>2016-11-07T12:32:03Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
This article describes the installation and usage of the EEGoSports SDK for the MNE-Scan project. &lt;br /&gt;
&lt;br /&gt;
== Building the EEGoSports EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
In order to build the EEGoSports driver plugin, the &amp;quot;eemagine&amp;quot; folder from the SDK has to be added to the EEGoSports folder in the mne-cpp repository in a first step. This location can be found in the mne-cpp repository under:&lt;br /&gt;
&lt;br /&gt;
'''%\mne-cpp\applications\mne_scan\plugins\eegosports\'''&lt;br /&gt;
&lt;br /&gt;
The &amp;quot;eemagine&amp;quot; folder includes another folder called &amp;quot;sdk&amp;quot; which holds the necessary header and source files like: amplifier.h, buffer.h, channel.h, exceptions.h, factory.h, stream.h, version.h, wrapper.cpp and wrapper.h.&lt;br /&gt;
&lt;br /&gt;
In a second step, the eegosports subproject has to be reintegrated into the mne-cpp project. This can be achieved by removing its comment status in the plugins' project file. This file can be found under following path:&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\applications\mne_scan\plugins.pro '''&lt;br /&gt;
&lt;br /&gt;
In order to reintegrate the eegosports subproject into mne-cpp, its comment status has to be removed in this file. This can be achieved by removing the # sign in front of the eegosports project in the Sensors category.&lt;br /&gt;
&lt;br /&gt;
Now, the whole mne-cpp can be rebuilt as normal. MNE Scan then will contain the EEGoSports driver plugin in its Sensor plugin section.&lt;br /&gt;
&lt;br /&gt;
== Running the EEGoSports EEG plugin ==&lt;br /&gt;
&lt;br /&gt;
For a correct recognition of the EEGoSports device, the operating system compatible driver has to be installed with the delivered software. Furthermore the two files &amp;quot;eego-SDK.lib&amp;quot; and &amp;quot;eego-SDK.dll&amp;quot; from the driver's library have to be copied to the location:&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\bin  '''&lt;br /&gt;
&lt;br /&gt;
In a last step, the license files which are delivered with the device have to be copied to&lt;br /&gt;
&lt;br /&gt;
''' C:\Users\''Username''\Documents\Eego '''.&lt;br /&gt;
&lt;br /&gt;
The license files should be named like &amp;quot;EE225-020032-000001&amp;quot;.&lt;br /&gt;
&lt;br /&gt;
After that, the device can be connected to the computer and be switched on and MNE Scan can be started. It is now possible to use the EEGoSports Driver plugin in the MNE Scan environment. By drag&amp;amp;drop the plugin can be added to the plugin box and connected to other processing items like shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
[[File:EEGoSportsGUI.jpg|center|900px|thumb| Configuration Window of the SSVEP-BCI plugin]]&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=File:EEGoSportsGUI.jpg&amp;diff=313</id>
		<title>File:EEGoSportsGUI.jpg</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=File:EEGoSportsGUI.jpg&amp;diff=313"/>
				<updated>2016-11-07T12:29:32Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: GUI of the EEGoSports Driver plugin.&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;GUI of the EEGoSports Driver plugin.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=EEGoSports&amp;diff=312</id>
		<title>EEGoSports</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=EEGoSports&amp;diff=312"/>
				<updated>2016-11-07T12:28:44Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
This article describes the installation and usage of the EEGoSports SDK for the MNE-Scan project. &lt;br /&gt;
&lt;br /&gt;
== Building the EEGoSports Driver plugin ==&lt;br /&gt;
&lt;br /&gt;
In order to build the EEGoSports driver plugin, the &amp;quot;eemagine&amp;quot; folder from the SDK has to be added to the EEGoSports folder in the mne-cpp repository in a first step. This location can be found in the mne-cpp repository under:&lt;br /&gt;
&lt;br /&gt;
'''%\mne-cpp\applications\mne_scan\plugins\eegosports\'''&lt;br /&gt;
&lt;br /&gt;
The &amp;quot;eemagine&amp;quot; folder includes another folder called &amp;quot;sdk&amp;quot; which holds the necessary header and source files like: amplifier.h, buffer.h, channel.h, exceptions.h, factory.h, stream.h, version.h, wrapper.cpp and wrapper.h.&lt;br /&gt;
&lt;br /&gt;
In a second step, the eegosports subproject has to be reintegrated into the mne-cpp project. This can be achieved by removing its comment status in the plugins' project file. This file can be found under following path:&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\applications\mne_scan\plugins.pro '''&lt;br /&gt;
&lt;br /&gt;
In order to reintegrate the eegosports subproject into mne-cpp, its comment status has to be removed in this file. This can be achieved by removing the # sign in front of the eegosports project in the Sensors category.&lt;br /&gt;
&lt;br /&gt;
Now, the whole mne-cpp can be rebuilt as normal. MNE Scan then will contain the EEGoSports driver plugin in its Sensor plugin section.&lt;br /&gt;
&lt;br /&gt;
== Running the EEGoSports Driver plugin ==&lt;br /&gt;
&lt;br /&gt;
For a correct recognition of the EEGoSports device, the operating system compatible driver has to be installed with the delivered software. Furthermore the two files &amp;quot;eego-SDK.lib&amp;quot; and &amp;quot;eego-SDK.dll&amp;quot; from the driver's library have to be copied to the location:&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\bin  '''&lt;br /&gt;
&lt;br /&gt;
In a last step, the license files which are delivered with the device have to be copied to&lt;br /&gt;
&lt;br /&gt;
''' C:\Users\''Username''\Documents\Eego '''.&lt;br /&gt;
&lt;br /&gt;
The license files should be named like &amp;quot;EE225-020032-000001&amp;quot;.&lt;br /&gt;
&lt;br /&gt;
After that, the device can be connected to the computer and be switched on and MNE Scan can be started. It is now possible to use the EEGoSports Driver plugin in the MNE Scan environment. By drag&amp;amp;drop the plugin can be added to the plugin box and connected to other processing items like shown in the following figure.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=EEGoSports&amp;diff=311</id>
		<title>EEGoSports</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=EEGoSports&amp;diff=311"/>
				<updated>2016-11-07T12:25:28Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
This article describes the installation and usage of the EEGoSports SDK for the MNE-Scan project. &lt;br /&gt;
&lt;br /&gt;
== Building the EEGoSports Driver plugin ==&lt;br /&gt;
&lt;br /&gt;
In order to build the EEGoSports driver plugin, the &amp;quot;eemagine&amp;quot; folder from the SDK has to be added to the EEGoSports folder in the mne-cpp repository in a first step. This location can be found in the mne-cpp repository under:&lt;br /&gt;
&lt;br /&gt;
'''%\mne-cpp\applications\mne_scan\plugins\eegosports\'''&lt;br /&gt;
&lt;br /&gt;
The &amp;quot;eemagine&amp;quot; folder includes another folder called &amp;quot;sdk&amp;quot; which holds the necessary header and source files like: amplifier.h, buffer.h, channel.h, exceptions.h, factory.h, stream.h, version.h, wrapper.cpp and wrapper.h.&lt;br /&gt;
&lt;br /&gt;
In a second step, the eegosports subproject has to be reintegrated into the mne-cpp project. This can be achieved by removing its comment status in the plugins' project file. This file can be found under following path:&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\applications\mne_scan\plugins.pro '''&lt;br /&gt;
&lt;br /&gt;
In order to reintegrate the eegosports subproject into mne-cpp, its comment status has to be removed in this file. This can be achieved by removing the # sign in front of the eegosports project in the Sensors category.&lt;br /&gt;
&lt;br /&gt;
Now, the whole mne-cpp can be rebuilt as normal. MNE Scan then will contain the EEGoSports driver plugin in its Sensor plugin section.&lt;br /&gt;
&lt;br /&gt;
== Running the EEGoSports Driver plugin ==&lt;br /&gt;
&lt;br /&gt;
For a correct recognition of the EEGoSports device, the operating system compatible driver has to be installed with the delivered software. Furthermore the two files &amp;quot;eego-SDK.lib&amp;quot; and &amp;quot;eego-SDK.dll&amp;quot; from the driver's library have to be copied to the location:&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\bin  '''&lt;br /&gt;
&lt;br /&gt;
In a last step, the license files which are delivered with the device have to be copied to&lt;br /&gt;
&lt;br /&gt;
''' C:\Users\''Username''\Documents\Eego '''.&lt;br /&gt;
&lt;br /&gt;
The license files should be named like &amp;quot;EE225-020032-000001&amp;quot;.&lt;br /&gt;
&lt;br /&gt;
After that, the device can be connected to the computer and be switched on and MNE Scan can be started. It is now possible to use the EEGoSports Driver plugin in the MNE Scan environment. By drag&amp;amp;drop the plugin can be added to the plugin box like in the following example with the SSVEP-BCI plugin.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=EEGoSports&amp;diff=310</id>
		<title>EEGoSports</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=EEGoSports&amp;diff=310"/>
				<updated>2016-11-07T10:26:30Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
This article describes the installation and usage of the EEGoSports SDK for the MNE-Scan project. &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Building the EEGoSports Driver plugin ==&lt;br /&gt;
&lt;br /&gt;
In order to build the EEGoSports driver plugin, the &amp;quot;eemagine&amp;quot; folder from the SDK has to be added to the EEGoSports folder in the mne-cpp repository in a first step. This location can be found in the mne-cpp repository under:&lt;br /&gt;
&lt;br /&gt;
'''%\mne-cpp\applications\mne_scan\plugins\eegosports\'''&lt;br /&gt;
&lt;br /&gt;
The &amp;quot;eemagine&amp;quot; folder includes another folder called &amp;quot;sdk&amp;quot; which holds the necessary header and source files like: amplifier.h, buffer.h, channel.h, exceptions.h, factory.h, stream.h, version.h, wrapper.cpp and wrapper.h.&lt;br /&gt;
&lt;br /&gt;
In a second step, the eegosports subproject has to be reintegrated into the mne-cpp project. This can be achieved by removing its comment status in the plugins' project file. This file can be found under following path:&lt;br /&gt;
&lt;br /&gt;
''' %\mne-cpp\applications\mne_scan\plugins.pro '''&lt;br /&gt;
&lt;br /&gt;
In order to reintegrate the eegosports subproject into mne-cpp, its comment status has to be removed. This can be achieved by removing the # sign in front of the eegosports project in the Sensors category.&lt;br /&gt;
&lt;br /&gt;
Now, the whole mne-cpp can be rebuilt as normal. MNE Scan will now contain the EEGoSports driver plugin in its Sensor plugin section.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=309</id>
		<title>Setup SSVEP BCI example</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=309"/>
				<updated>2016-11-05T14:40:13Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
The SSVEP-BCI plugin beholds a Brain Computer Interface (BCI) using Steady-State-Visual-Evoked-Potentials (SSVEP). It was developed under the Master Thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot;, in which the so called [http://iopscience.iop.org/article/10.1088/1741-2560/8/3/036020/meta Bremen-BCI] was used as a role model in order to setup classification pipeline and visualization. This tutorial describes both the setup of the processing pipeline and usage of the SSVEP-BCI plugin.&lt;br /&gt;
&lt;br /&gt;
== Editing the Processing Pipeline ==&lt;br /&gt;
&lt;br /&gt;
In the following figure an edited setup of the SSVEP-BCI processing pipeline is shown in the MNE Scan GUI environment. For this example the NoiseReduction plugin was interposed as a preprocessing step in order to increase the Signal-to-Noise ratio. However, the SSVEP-BCI plugin can also be processed with &amp;quot;raw&amp;quot; (unfiltered) data by connecting it directly with the  acquisition plugin.&lt;br /&gt;
&lt;br /&gt;
[[File:MNE-Scan processing pipelilne.jpeg|frame|center|SSVEP BCI processing pipeline in the MNE Scan environment]]&lt;br /&gt;
&lt;br /&gt;
== Adjusting the SSVEP-BCI ==&lt;br /&gt;
&lt;br /&gt;
Like already mentioned, in the thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot; different scientific approaches are combined in order to realize this BCI. The adjustment of these features can be done by clicking the configuration icon:[[File:Configuration.png|20px|link=]] on the SSVEP-BCI's GUI which is depictured in the previous figure. With this command, the configuration GUI is opened which is shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
[[File:SSVEPConfigurationWindow.jpg|center|900px|thumb| Configuration Window of the SSVEP-BCI plugin]]&lt;br /&gt;
&lt;br /&gt;
The adjustments can be done online during the processing of the plugin with the configuration GUI:&lt;br /&gt;
&lt;br /&gt;
===== Channel Select =====&lt;br /&gt;
The channel select is realized with a .txt-file which is located under &amp;quot;''MNE-CPP Folder''-bin-mne_scan_plugins-resources-ssvepBCI&amp;quot;, like the already included &amp;quot;Pinning-Scheme-Duke-Dry-64.txt&amp;quot; file. These files behold the mapping of the channel names according to the channel number in the FIFF-file. You can change the current selected folder or link the plugin to a new edited file.&lt;br /&gt;
In the latter case, the new edited file-name has to be changed in line 241 of the &amp;quot;ssvepbciconfiguration.cpp&amp;quot; file. Also the default channels can be changed in the ssvepbci.cpp file in line 104 and 106.&lt;br /&gt;
Now the channel can be selected or deleted by drag&amp;amp;drop between the available- and delete-section&lt;br /&gt;
&lt;br /&gt;
===== Feature Extraction =====&lt;br /&gt;
&lt;br /&gt;
The feature extraction can be realized with either the MEC or the CCA algorithm. Both approaches are described in the corresponding [http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization thesis]. Also the number of generated harmonics of the reference signal can be set in this frame.&lt;br /&gt;
&lt;br /&gt;
===== Classification =====&lt;br /&gt;
&lt;br /&gt;
The state of classification is displayed with five different progress bars belonging each to a different SSVEP frequency. The thresholds can be set with the double-spin-boxes by moving the slider (threshold) up and down. &lt;br /&gt;
&lt;br /&gt;
For a classification indeed to be confirmed, the classification has to be done several times in a sliding list. Following parameter can be set for this process:&lt;br /&gt;
* '''Number of Classification Hits''': is the minimal number of classification hits until there is a confirmation.&lt;br /&gt;
* '''Size of Classification''': is the size of possible entries in to the classification list. By logic, this value is always bigger or equal than the &amp;quot;Number of Classification Hits&amp;quot;.&lt;br /&gt;
* '''Number of Classification Breaks''': Since there are a lot of subject dependent variations, the algorithm needs to be paused between two classifications. Therefore a number of classification breaks can be set with this parameter.&lt;br /&gt;
If a classification has been confirmed, its name will be displayed red and also can be seen in the LCD-number field.&lt;br /&gt;
&lt;br /&gt;
===== Accuracy Feature =====&lt;br /&gt;
&lt;br /&gt;
In order to determine accuracy and robustness of the plugin, a measurement can be started with the screen keyboard device. For this purpose a Measurement can be started and stopped in the corresponding frame. However, the screen keyboard has to be set before which is explained in the following section.&lt;br /&gt;
&lt;br /&gt;
== Adjusting the Stimulation ==&lt;br /&gt;
&lt;br /&gt;
For stimulation, a second screen hast to be attached to the control computer. The BCI then will adapt automatically to the adjusted frequencies. The configuration of the stimulation can be set by clicking the stimulation icon: [[File:Stimulus.png|20px|link=]]. Consequently, the window for setting the stimulation opens.&lt;br /&gt;
&lt;br /&gt;
[[File:StimulationFeature.jpg|center|900px|thumb| Stimulation feature of the SSVEP-BCI plugin.]]&lt;br /&gt;
&lt;br /&gt;
Three tests can be started by clicking the associated buttons. Every test beholds different numbers of flickering panels with a different size. Each panel can be selected with the first combo box. With the second combo box, the frequency of the selected panel can be adjusted. Below of the combo boxes, the refresh rate of the subject's screen is displayed. If the stimulation screen is shown on the wrong monitor, the screens' numbers have to be exchanged in the main settings of the operating system.&lt;br /&gt;
&lt;br /&gt;
The fourth button initializes the visualization of the screen keyboard device. In the group box &amp;quot;BCI Speller&amp;quot; a short phrase can be set in order to show to the subject in reinitialize the accuracy feature. In this example the chosen phrase is &amp;quot;TU Ilmenau&amp;quot;. By clicking the button &amp;quot;start measurement&amp;quot; in the SSVEP-BCI configuration window. A measurement can be started then.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Handling of the Screen Keyboard ==&lt;br /&gt;
&lt;br /&gt;
The Screen Keyboard is a special application for SSVEP-BCIs which was designed for the [http://iopscience.iop.org/article/10.1088/1741-2560/8/3/036020/meta Bremen-BCI]. Following figure shows the surface of the subject's screen. &lt;br /&gt;
&lt;br /&gt;
[[File:ScreenKeyboard.jpg|center|900px|thumb| Stimulation feature of the SSVEP-BCI plugin.]]&lt;br /&gt;
&lt;br /&gt;
The subject can control the Screen Keyboard by concentrating of the four direction- and the &amp;quot;Select&amp;quot; panel. The BCI recognizes the selected command and translates them to the red cursor in the middle of the screen. The bottom left corner shows the phrase which is supposed to be spelled. This phrase can be set with the stimulation feature. The accuracy feature then will count the amount of right and wrong commands.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=EEGoSports&amp;diff=308</id>
		<title>EEGoSports</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=EEGoSports&amp;diff=308"/>
				<updated>2016-11-03T16:00:42Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: Created page with &amp;quot;== Abstract ==  This article describes the installation and usage of the EEGoSports SDK for the MNE-Scan project.    == Building the EEGoSports Driver plugin ==&amp;quot;&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
This article describes the installation and usage of the EEGoSports SDK for the MNE-Scan project. &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Building the EEGoSports Driver plugin ==&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=307</id>
		<title>Setup SSVEP BCI example</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=307"/>
				<updated>2016-11-03T15:14:49Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
The SSVEP-BCI plugin beholds a Brain Computer Interface (BCI) using Steady-State-Visual-Evoked-Potentials (SSVEP). It was developed under the Master Thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot;, in which the so called [http://iopscience.iop.org/article/10.1088/1741-2560/8/3/036020/meta Bremen-BCI] was used as a role model in order to setup classification pipeline and visualization. This tutorial describes both the setup of the processing pipeline and usage of the SSVEP-BCI plugin.&lt;br /&gt;
&lt;br /&gt;
== Editing the Processing Pipeline ==&lt;br /&gt;
&lt;br /&gt;
In the following figure an edited setup of the SSVEP-BCI processing pipeline is shown in the MNE-Scan GUI environment. For this example the NoiseReduction plugin was interposed as a preprocessing step in order to increase the Signal-to-Noise ratio. However, the SSVEP-BCI plugin can also be processed with &amp;quot;raw&amp;quot; (unfiltered) data by connecting it directly with the  acquisition plugin.&lt;br /&gt;
&lt;br /&gt;
[[File:MNE-Scan processing pipelilne.jpeg|frame|center|SSVEP BCI processing pipeline in the MNE-Scan environment]]&lt;br /&gt;
&lt;br /&gt;
== Adjusting the SSVEP-BCI ==&lt;br /&gt;
&lt;br /&gt;
Like already mentioned, in the thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot; different scientific approaches are combined in order to realize this BCI. The adjustment of these features can be done by clicking the configuration icon:[[File:Configuration.png|20px|link=]] on the SSVEP-BCI's GUI which is depictured in the previous figure. With this command, the configuration GUI is opened which is shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
[[File:SSVEPConfigurationWindow.jpg|center|900px|thumb| Configuration Window of the SSVEP-BCI plugin]]&lt;br /&gt;
&lt;br /&gt;
The adjustments can be done online during the processing of the plugin with the configuration GUI:&lt;br /&gt;
&lt;br /&gt;
===== Channel Select =====&lt;br /&gt;
The channel select is realized with a .txt-file which is located under &amp;quot;''MNE-CPP Folder''-bin-mne_scan_plugins-resources-ssvepBCI&amp;quot;, like the already included &amp;quot;Pinning-Scheme-Duke-Dry-64.txt&amp;quot; file. These files behold the mapping of the channel names according to the channel number in the FIFF-file. You can change the current selected folder or link the plugin to a new edited file.&lt;br /&gt;
In the latter case, the new edited file-name has to be changed in line 241 of the &amp;quot;ssvepbciconfiguration.cpp&amp;quot; file. Also the default channels can be changed in the ssvepbci.cpp file in line 104 and 106.&lt;br /&gt;
Now the channel can be selected or deleted by drag&amp;amp;drop between the available- and delete-section&lt;br /&gt;
&lt;br /&gt;
===== Feature Extraction =====&lt;br /&gt;
&lt;br /&gt;
The feature extraction can be realized with either the MEC or the CCA algorithm. Both approaches are described in the corresponding [http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization thesis]. Also the number of generated harmonics of the reference signal can be set in this frame.&lt;br /&gt;
&lt;br /&gt;
===== Classification =====&lt;br /&gt;
&lt;br /&gt;
The state of classification is displayed with five different progress bars belonging each to a different SSVEP frequency. The thresholds can be set with the double-spin-boxes by moving the slider (threshold) up and down. &lt;br /&gt;
&lt;br /&gt;
For a classification indeed to be confirmed, the classification has to be done several times in a sliding list. Following parameter can be set for this process:&lt;br /&gt;
* '''Number of Classification Hits''': is the minimal number of classification hits until there is a confirmation.&lt;br /&gt;
* '''Size of Classification''': is the size of possible entries in to the classification list. By logic, this value is always bigger or equal than the &amp;quot;Number of Classification Hits&amp;quot;.&lt;br /&gt;
* '''Number of Classification Breaks''': Since there are a lot of subject dependent variations, the algorithm needs to be paused between two classifications. Therefore a number of classification breaks can be set with this parameter.&lt;br /&gt;
If a classification has been confirmed, its name will be displayed red and also can be seen in the LCD-number field.&lt;br /&gt;
&lt;br /&gt;
===== Accuracy Feature =====&lt;br /&gt;
&lt;br /&gt;
In order to determine accuracy and robustness of the plugin, a measurement can be started with the screen keyboard device. For this purpose a Measurement can be started and stopped in the corresponding frame. However, the screen keyboard has to be set before which is explained in the following section.&lt;br /&gt;
&lt;br /&gt;
== Adjusting the Stimulation ==&lt;br /&gt;
&lt;br /&gt;
For stimulation, a second screen hast to be attached to the control computer. The BCI then will adapt automatically to the adjusted frequencies. The configuration of the stimulation can be set by clicking the stimulation icon: [[File:Stimulus.png|20px|link=]]. Consequently, the window for setting the stimulation opens.&lt;br /&gt;
&lt;br /&gt;
[[File:StimulationFeature.jpg|center|900px|thumb| Stimulation feature of the SSVEP-BCI plugin.]]&lt;br /&gt;
&lt;br /&gt;
Three tests can be started by clicking the associated buttons. Every test beholds different numbers of flickering panels with a different size. Each panel can be selected with the first combo box. With the second combo box, the frequency of the selected panel can be adjusted. Below of the combo boxes, the refresh rate of the subject's screen is displayed. If the stimulation screen is shown on the wrong monitor, the screens' numbers have to be exchanged in the main settings of the operating system.&lt;br /&gt;
&lt;br /&gt;
The fourth button initializes the visualization of the screen keyboard device. In the group box &amp;quot;BCI Speller&amp;quot; a short phrase can be set in order to show to the subject in reinitialize the accuracy feature. In this example the chosen phrase is &amp;quot;TU Ilmenau&amp;quot;. By clicking the button &amp;quot;start measurement&amp;quot; in the SSVEP-BCI configuration window. A measurement can be started then.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Handling of the Screen Keyboard ==&lt;br /&gt;
&lt;br /&gt;
The Screen Keyboard is a special application for SSVEP-BCIs which was designed for the [http://iopscience.iop.org/article/10.1088/1741-2560/8/3/036020/meta Bremen-BCI]. Following figure shows the surface of the subject's screen. &lt;br /&gt;
&lt;br /&gt;
[[File:ScreenKeyboard.jpg|center|900px|thumb| Stimulation feature of the SSVEP-BCI plugin.]]&lt;br /&gt;
&lt;br /&gt;
The subject can control the Screen Keyboard by concentrating of the four direction- and the &amp;quot;Select&amp;quot; panel. The BCI recognizes the selected command and translates them to the red cursor in the middle of the screen. The bottom left corner shows the phrase which is supposed to be spelled. This phrase can be set with the stimulation feature. The accuracy feature then will count the amount of right and wrong commands.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=File:ScreenKeyboard.jpg&amp;diff=306</id>
		<title>File:ScreenKeyboard.jpg</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=File:ScreenKeyboard.jpg&amp;diff=306"/>
				<updated>2016-11-03T14:44:47Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: The surface on the subject's screen of the Screen Keyboard device. The four direction panels, as well as the &amp;quot;Select&amp;quot; panel are flickering with different frequencies. By concentrating on one of the panels the BCI recognizes the command and translates i...&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;The surface on the subject's screen of the Screen Keyboard device. The four direction panels, as well as the &amp;quot;Select&amp;quot; panel are flickering with different frequencies. By concentrating on one of the panels the BCI recognizes the command and translates it. In the bottom left corner a word is shown which is supposed to be spelled.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=File:StimulationFeature.jpg&amp;diff=305</id>
		<title>File:StimulationFeature.jpg</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=File:StimulationFeature.jpg&amp;diff=305"/>
				<updated>2016-11-03T14:08:16Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: control window of the stimulation from the SSVEP-BCI plugin&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;control window of the stimulation from the SSVEP-BCI plugin&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=304</id>
		<title>Setup SSVEP BCI example</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=304"/>
				<updated>2016-11-03T11:06:44Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
The SSVEP-BCI plugin beholds a Brain Computer Interface (BCI) using Steady-State-Visual-Evoked-Potentials (SSVEP). It was developed under the Master Thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot;, in which the so called [http://iopscience.iop.org/article/10.1088/1741-2560/8/3/036020/meta Bremen-BCI] was used as a role model in order to setup classification pipeline and visualization. This tutorial describes both the setup of the processing pipeline and usage of the SSVEP-BCI plugin.&lt;br /&gt;
&lt;br /&gt;
== Editing the Processing Pipeline ==&lt;br /&gt;
&lt;br /&gt;
In the following figure an edited setup of the SSVEP-BCI processing pipeline is shown in the MNE-Scan GUI environment. For this example the NoiseReduction plugin was interposed as a preprocessing step in order to increase the Signal-to-Noise ratio. However, the SSVEP-BCI plugin can also be processed with &amp;quot;raw&amp;quot; (unfiltered) data by connecting it directly with the  acquisition plugin.&lt;br /&gt;
&lt;br /&gt;
[[File:MNE-Scan processing pipelilne.jpeg|frame|center|SSVEP BCI processing pipeline in the MNE-Scan environment]]&lt;br /&gt;
&lt;br /&gt;
== Adjusting the SSVEP-BCI ==&lt;br /&gt;
&lt;br /&gt;
Like already mentioned, in the thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot; different scientific approaches are combined in order to realize this BCI. The adjustment of these features can be done by clicking the configuration icon:[[File:Configuration.png|20px|link=]] on the SSVEP-BCI's GUI which is depictured in the previous figure. With this command, the configuration GUI is opened which is shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
[[File:SSVEPConfigurationWindow.jpg|center|900px|thumb| Configuration Window of the SSVEP-BCI plugin]]&lt;br /&gt;
&lt;br /&gt;
The adjustments can be done online during the processing of the plugin with the configuration GUI:&lt;br /&gt;
&lt;br /&gt;
===== Channel Select =====&lt;br /&gt;
The channel select is realized with a .txt-file which is located under &amp;quot;''MNE-CPP Folder''-bin-mne_scan_plugins-resources-ssvepBCI&amp;quot;, like the already included &amp;quot;Pinning-Scheme-Duke-Dry-64.txt&amp;quot; file. These files behold the mapping of the channel names according to the channel number in the FIFF-file. You can change the current selected folder or link the plugin to a new edited file.&lt;br /&gt;
In the latter case, the new edited file-name has to be changed in line 241 of the &amp;quot;ssvepbciconfiguration.cpp&amp;quot; file. Also the default channels can be changed in the ssvepbci.cpp file in line 104 and 106.&lt;br /&gt;
Now the channel can be selected or deleted by drag&amp;amp;drop between the available- and delete-section&lt;br /&gt;
&lt;br /&gt;
===== Feature Extraction =====&lt;br /&gt;
&lt;br /&gt;
The feature extraction can be realized with either the MEC or the CCA algorithm. Both approaches are described in the corresponding [http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization thesis]. Also the number of generated harmonics of the reference signal can be set in this frame.&lt;br /&gt;
&lt;br /&gt;
===== Classification =====&lt;br /&gt;
&lt;br /&gt;
The state of classification is displayed with five different progress bars belonging each to a different SSVEP frequency. The thresholds can be set with the double-spin-boxes by moving the slider (threshold) up and down. &lt;br /&gt;
&lt;br /&gt;
For a classification indeed to be confirmed, the classification has to be done several times in a sliding list. Following parameter can be set for this process:&lt;br /&gt;
* '''Number of Classification Hits''': is the minimal number of classification hits until there is a confirmation.&lt;br /&gt;
* '''Size of Classification''': is the size of possible entries in to the classification list. By logic, this value is always bigger or equal than the &amp;quot;Number of Classification Hits&amp;quot;.&lt;br /&gt;
* '''Number of Classification Breaks''': Since there are a lot of subject dependent variations, the algorithm needs to be paused between two classifications. Therefore a number of classification breaks can be set with this parameter.&lt;br /&gt;
If a classification has been confirmed, its name will be displayed red and also can be seen in the LCD-number field.&lt;br /&gt;
&lt;br /&gt;
===== Accuracy Feature =====&lt;br /&gt;
&lt;br /&gt;
In order to determine accuracy and robustness of the plugin, a measurement can be started with the screen keyboard device. For this purpose a Measurement can be started and stopped in the corresponding frame. However, the screen keyboard has to be set before which is explained in the following section.&lt;br /&gt;
&lt;br /&gt;
== Adjusting the Stimulation ==&lt;br /&gt;
&lt;br /&gt;
For stimulation, a second screen hast to be attached to the control computer. The BCI then will adapt automatically to the adjusted frequencies. The configuration of the stimulation can be set by clicking the stimulation icon: [[File:Stimulus.png|20px|link=]]. Consequently, the window for setting the stimulation opens.&lt;br /&gt;
&lt;br /&gt;
Three tests can be started by clicking the associated buttons. Every test beholds different numbers of flickering panels with a different size. Each panel can be selected with the first combo box. With the second combo box, the frequency of the selected panel can be adjusted. Below of the combo boxes, the refresh rate of the subject's screen is displayed. If the stimulation screen is shown on the wrong monitor, the screens' numbers have to be exchanged in the main settings of the operating system.&lt;br /&gt;
&lt;br /&gt;
The fourth button initializes the visualization of the screen keyboard device. In the group box &amp;quot;BCI Speller&amp;quot; a short phrase can be set in order to show to the subject in reinitialize the accuracy feature. By clicking the button &amp;quot;start measurement&amp;quot; ind the SSVEP-BCI configuration window. A measurement can be started then.&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=303</id>
		<title>Setup SSVEP BCI example</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=303"/>
				<updated>2016-10-29T07:15:56Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
The SSVEP-BCI plugin beholds a Brain Computer Interface (BCI) using Steady-State-Visual-Evoked-Potentials (SSVEP). It was developed under the Master Thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot;, in which the so called [http://iopscience.iop.org/article/10.1088/1741-2560/8/3/036020/meta Bremen-BCI] was used as a role model in order to setup classification pipeline and visualization. This tutorial describes both the setup of the processing pipeline and usage of the SSVEP-BCI plugin.&lt;br /&gt;
&lt;br /&gt;
== Editing the Processing Pipeline ==&lt;br /&gt;
&lt;br /&gt;
In the following figure an edited setup of the SSVEP-BCI processing pipeline is shown in the MNE-Scan GUI environment. For this example the NoiseReduction plugin was interposed as a preprocessing step in order to increase the Signal-to-Noise ratio. However, the SSVEP-BCI plugin can also be processed with &amp;quot;raw&amp;quot; (unfiltered) data by connecting it directly with the  acquisition plugin.&lt;br /&gt;
&lt;br /&gt;
[[File:MNE-Scan processing pipelilne.jpeg|frame|center|SSVEP BCI processing pipeline in the MNE-Scan environment]]&lt;br /&gt;
&lt;br /&gt;
== Adjusting the SSVEP-BCI ==&lt;br /&gt;
&lt;br /&gt;
Like already mentioned, in the thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot; different scientific approaches are combined in order to realize this BCI. The adjustment of these features can be done by clicking the configuration icon:[[File:Configuration.png|20px|link=]] on the SSVEP-BCI's GUI which is depictured in the previous figure. With this command, the configuration GUI is opened which is shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
[[File:SSVEPConfigurationWindow.jpg|center|900px|thumb| Configuration Window of the SSVEP-BCI plugin]]&lt;br /&gt;
&lt;br /&gt;
The adjustments can be done online during the processing of the plugin with the configuration GUI:&lt;br /&gt;
&lt;br /&gt;
===== Channel Select =====&lt;br /&gt;
The channel select is realized with a .txt-file which is located under &amp;quot;''MNE-CPP Folder''-bin-mne_scan_plugins-resources-ssvepBCI&amp;quot;, like the already included &amp;quot;Pinning-Scheme-Duke-Dry-64.txt&amp;quot; file. These files behold the mapping of the channel names according to the channel number in the FIFF-file. You can change the current selected folder or link the plugin to a new edited file.&lt;br /&gt;
In the latter case, the new edited file-name has to be changed in line 241 of the &amp;quot;ssvepbciconfiguration.cpp&amp;quot; file. Also the default channels can be changed in the ssvepbci.cpp file in line 104 and 106.&lt;br /&gt;
Now the channel can be selected or deleted by drag&amp;amp;drop between the available- and delete-section&lt;br /&gt;
&lt;br /&gt;
===== Feature Extraction =====&lt;br /&gt;
&lt;br /&gt;
The feature extraction can be realized with either the MEC or the CCA algorithm. Both approaches are described in the corresponding [http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization thesis]. Also the number of generated harmonics of the reference signal can be set in this frame.&lt;br /&gt;
&lt;br /&gt;
===== Classification =====&lt;br /&gt;
&lt;br /&gt;
The state of classification is displayed with five different progress bars belonging each to a different SSVEP frequency. The thresholds can be set with the double-spin-boxes by moving the slider (threshold) up and down. &lt;br /&gt;
&lt;br /&gt;
For a classification indeed to be confirmed, the classification has to be done several times in a sliding list. Following parameter can be set for this process:&lt;br /&gt;
* '''Number of Classification Hits''': is the minimal number of classification hits until there is a confirmation.&lt;br /&gt;
* '''Size of Classification''': is the size of possible entries in to the classification list. By logic, this value is always bigger or equal than the &amp;quot;Number of Classification Hits&amp;quot;.&lt;br /&gt;
* '''Number of Classification Breaks''': Since there are a lot of subject dependent variations, the algorithm needs to be paused between two classifications. Therefore a number of classification breaks can be set with this parameter.&lt;br /&gt;
If a classification has been confirmed, its name will be displayed red and also can be seen in the LCD-number field.&lt;br /&gt;
&lt;br /&gt;
===== Accuracy Feature =====&lt;br /&gt;
&lt;br /&gt;
In order to determine accuracy and robustness of the plugin, a measurement can be started with the screen keyboard device. For this purpose a Measurement can be started and stopped in the corresponding frame. However, the screen keyboard has to be set before which is explained in the following section.&lt;br /&gt;
&lt;br /&gt;
== Adjusting the Stimulation ==&lt;br /&gt;
&lt;br /&gt;
The configuration of the stimulation can be set by clicking the stimulation icon: [[File:Stimulus.png|20px|link=]]&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=302</id>
		<title>Setup SSVEP BCI example</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=302"/>
				<updated>2016-10-27T15:16:07Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
The SSVEP-BCI plugin beholds a Brain Computer Interface (BCI) using Steady-State-Visual-Evoked-Potentials (SSVEP). It was developed under the Master Thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot;, in which the so called [http://iopscience.iop.org/article/10.1088/1741-2560/8/3/036020/meta Bremen-BCI] was used as a role model in order to setup classification pipeline and visualization. This tutorial describes both the setup of the processing pipeline and usage of the SSVEP-BCI plugin.&lt;br /&gt;
&lt;br /&gt;
== Editing the Processing Pipeline ==&lt;br /&gt;
&lt;br /&gt;
In the following figure an edited setup of the SSVEP-BCI processing pipeline is shown in the MNE-Scan GUI environment. For this example the NoiseReduction plugin was interposed as a preprocessing step in order to increase the Signal-to-Noise ratio. However, the SSVEP-BCI plugin can also be processed with &amp;quot;raw&amp;quot; (unfiltered) data by connecting it directly with the  acquisition plugin.&lt;br /&gt;
&lt;br /&gt;
[[File:MNE-Scan processing pipelilne.jpeg|frame|center|SSVEP BCI processing pipeline in the MNE-Scan environment]]&lt;br /&gt;
&lt;br /&gt;
== Adjusting the SSVEP-BCI ==&lt;br /&gt;
&lt;br /&gt;
Like already mentioned, in the thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot; different scientific approaches are combined in order to realize this BCI. The adjustment of these features can be done by clicking the configuration icon:[[File:Configuration.png|20px|link=]] on the SSVEP-BCI's GUI which is depictured in the previous figure. With this command, the configuration GUI is opened which is shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
[[File:SSVEPConfigurationWindow.jpg|right|900px|thumb| Configuration Window of the SSVEP-BCI plugin]]&lt;br /&gt;
&lt;br /&gt;
The adjustments can be done online during the processing of the plugin with the configuration GUI:&lt;br /&gt;
&lt;br /&gt;
===== Channel Select =====&lt;br /&gt;
The channel select is realized with a .txt-file which is located under &amp;quot;''MNE-CPP Folder''-bin-mne_scan_plugins-resources-ssvepBCI&amp;quot;, like the already included &amp;quot;Pinning-Scheme-Duke-Dry-64.txt&amp;quot; file. These files behold the mapping of the channel names according to the channel number in the FIFF-file. You can change the current selected folder or link the plugin to a new edited file.&lt;br /&gt;
In the latter case, the new edited file-name has to be changed in line 241 of the &amp;quot;ssvepbciconfiguration.cpp&amp;quot; file. Also the default channels can be changed in the ssvepbci.cpp file in line 104 and 106.&lt;br /&gt;
Now the channel can be selected or deleted by drag&amp;amp;drop between the available- and delete-section&lt;br /&gt;
&lt;br /&gt;
===== Feature Extraction =====&lt;br /&gt;
&lt;br /&gt;
The feature extraction can be realized with either the MEC or the CCA algorithm. Both approaches are described in the corresponding [http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization thesis]. Also the number of generated harmonics of the reference signal can be set in this frame.&lt;br /&gt;
&lt;br /&gt;
===== Classification =====&lt;br /&gt;
&lt;br /&gt;
The state of classification is displayed with five different progress bars belonging each to a different SSVEP frequency. The thresholds can be set with the double-spin-boxes by moving the slider (threshold) up and down. &lt;br /&gt;
&lt;br /&gt;
For a classification indeed to be confirmed, the classification has to be done several times in a sliding list. Following parameter can be set for this process:&lt;br /&gt;
* '''Number of Classification Hits''': is the minimal number of classification hits until there is a confirmation.&lt;br /&gt;
* '''Size of Classification''': is the size of possible entries in to the classification list. By logic, this value is always bigger or equal than the &amp;quot;Number of Classification Hits&amp;quot;.&lt;br /&gt;
* '''Number of Classification Breaks''': Since there are a lot of subject dependent variations, the algorithm needs to be paused between two classifications. Therefore a number of classification breaks can be set with this parameter.&lt;br /&gt;
If a classification has been confirmed, its name will be displayed red and also can be seen in the LCD-number field.&lt;br /&gt;
&lt;br /&gt;
===== Accuracy Feature =====&lt;br /&gt;
&lt;br /&gt;
In order to determine accuracy and robustness of the plugin, a measurement can be started with the screen keyboard device. For this purpose a Measurement can be started and stopped in the corresponding frame. However, the screen keyboard has to be set before which is explained in the following section.&lt;br /&gt;
&lt;br /&gt;
== Adjusting the Stimulation ==&lt;br /&gt;
&lt;br /&gt;
The configuration of the stimulation can be set by clicking the stimulation icon: [[File:Stimulus.png|20px|link=]]&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=301</id>
		<title>Setup SSVEP BCI example</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=301"/>
				<updated>2016-10-27T14:12:14Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
The SSVEP-BCI plugin beholds a Brain Computer Interface (BCI) using Steady-State-Visual-Evoked-Potentials (SSVEP). It was developed under the Master Thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot;, in which the so called [http://iopscience.iop.org/article/10.1088/1741-2560/8/3/036020/meta Bremen-BCI] was used as a role model in order to setup classification pipeline and visualization. This tutorial describes both the setup of the processing pipeline and usage of the SSVEP-BCI plugin.&lt;br /&gt;
&lt;br /&gt;
== Editing the Processing Pipeline ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
In the following figure an edited setup of the SSVEP-BCI processing pipeline is shown in the MNE-Scan GUI environment. For this example the NoiseReduction plugin was interposed as a preprocessing step in order to increase the Signal-to-Noise ratio. However, the SSVEP-BCI plugin can also be processed with &amp;quot;raw&amp;quot; (unfiltered) data by connecting it directly with the  acquisition plugin.&lt;br /&gt;
&lt;br /&gt;
[[File:MNE-Scan processing pipelilne.jpeg|frame|center|SSVEP BCI processing pipeline in the MNE-Scan environment]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Adjusting the SSVEP-BCI ==&lt;br /&gt;
&lt;br /&gt;
Like already mentioned, in the thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot; different scientific approaches are combined in order to realize this BCI. The adjustment of these features can be done by clicking the configuration icon:[[File:Configuration.png|20px|link=]] on the SSVEP-BCI's GUI which is depictured in the previous figure. With this command, the configuration GUI is opened which is shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
[[File:SSVEPConfigurationWindow.jpg|right|900px|thumb| Configuration Window of the SSVEP-BCI plugin]]&lt;br /&gt;
&lt;br /&gt;
Following adjustment can be done with the configuration GUI:&lt;br /&gt;
&lt;br /&gt;
===== Channel Select =====&lt;br /&gt;
The channel select is realized with a .txt-file which is located under &amp;quot;''MNE-CPP Folder''-bin-mne_scan_plugins-resources-ssvepBCI&amp;quot;, like the already included &amp;quot;Pinning_Scheme_Duke_Dry_64.txt&amp;quot; file. These files behold the mapping of the channel names according to the channel number in the FIFF-file. You can change the current selected folder or link the plugin to a new edited file.&lt;br /&gt;
In the latter case, the new edited file-name has to be changed in line 241 of the &amp;quot;ssvepbciconfiguration.cpp&amp;quot; file. Also the default channels can be changed in the ssvepbci.cpp file in line 104 and 106.&lt;br /&gt;
Now the channel can be selected or deleted by drag&amp;amp;drop between the available- and delete-section&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===== Feature Extraction =====&lt;br /&gt;
&lt;br /&gt;
The feature extraction can be realized with either the MEC or the CCA algorithm. Both approaches are described in the corresponding [http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization thesis].&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=300</id>
		<title>Setup SSVEP BCI example</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=300"/>
				<updated>2016-10-27T13:04:59Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
The SSVEP-BCI plugin beholds a Brain Computer Interface (BCI) using Steady-State-Visual-Evoked-Potentials (SSVEP). It was developed under the Master Thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot;, in which the so called [http://iopscience.iop.org/article/10.1088/1741-2560/8/3/036020/meta Bremen-BCI] was used as a role model in order to setup classification pipeline and visualization. This tutorial describes both the setup of the processing pipeline and usage of the SSVEP-BCI plugin.&lt;br /&gt;
&lt;br /&gt;
== Editing the Processing Pipeline ==&lt;br /&gt;
&lt;br /&gt;
In the following figure an edited setup of the SSVEP-BCI processing pipeline is shown in the MNE-Scan GUI environment. For this example the NoiseReduction plugin was interposed as a preprocessing step in order to increase the Signal-to-Noise ratio. However, the SSVEP-BCI plugin can also be processed with &amp;quot;raw&amp;quot; (unfiltered) data by connecting it directly with the  acquisition plugin.&lt;br /&gt;
&lt;br /&gt;
[[File:MNE-Scan processing pipelilne.jpeg|framed|center|SSVEP BCI processing pipeline in the MNE-Scan environment]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Adjusting the SSVEP-BCI ==&lt;br /&gt;
&lt;br /&gt;
Like already mentioned, in the thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot; different scientific approaches are combined in order to realize this BCI. The adjustment of these features can be done by pressing on the configuration icon:[[File:Configuration.png|20px|link=]] on the SSVEP-BCI's GUI which is depictured in the previous figure. With this command, the configuration GUI is opened which is shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[[File:SSVEPConfigurationWindow.jpg|right|200px| Configuration Window of the SSVEP-BCI plugin]]&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=299</id>
		<title>Setup SSVEP BCI example</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=299"/>
				<updated>2016-10-27T13:01:48Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: /* Adjusting the SSVEP-BCI */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
The SSVEP-BCI plugin beholds a Brain Computer Interface (BCI) using Steady-State-Visual-Evoked-Potentials (SSVEP). It was developed under the Master Thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot;, in which the so called [http://iopscience.iop.org/article/10.1088/1741-2560/8/3/036020/meta Bremen-BCI] was used as a role model in order to setup classification pipeline and visualization. This tutorial describes both the setup of the processing pipeline and usage of the SSVEP-BCI plugin.&lt;br /&gt;
&lt;br /&gt;
== Editing the Processing Pipeline ==&lt;br /&gt;
&lt;br /&gt;
In the following figure an edited setup of the SSVEP-BCI processing pipeline is shown in the MNE-Scan GUI environment. For this example the NoiseReduction plugin was interposed as a preprocessing step in order to increase the Signal-to-Noise ratio. However, the SSVEP-BCI plugin can also be processed with &amp;quot;raw&amp;quot; (unfiltered) data by connecting it directly with the  acquisition plugin.&lt;br /&gt;
&lt;br /&gt;
[[File:MNE-Scan processing pipelilne.jpeg|framed|center|SSVEP BCI processing pipeline in the MNE-Scan environment]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Adjusting the SSVEP-BCI ==&lt;br /&gt;
&lt;br /&gt;
Like already mentioned, in the thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot; different scientific approaches are combined in order to realize this BCI. The adjustment of these features can be done by pressing on the configuration icon:[[File:Configuration.png|20px|link=]] on the SSVEP-BCI's GUI which is depictured in the previous figure. With this command, the configuration GUI is opened which is shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[[File:SSVEPConfigurationWindow.jpg|10px|framed|link=]]&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=298</id>
		<title>Setup SSVEP BCI example</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=298"/>
				<updated>2016-10-27T13:01:26Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: /* Adjusting the SSVEP-BCI */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
The SSVEP-BCI plugin beholds a Brain Computer Interface (BCI) using Steady-State-Visual-Evoked-Potentials (SSVEP). It was developed under the Master Thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot;, in which the so called [http://iopscience.iop.org/article/10.1088/1741-2560/8/3/036020/meta Bremen-BCI] was used as a role model in order to setup classification pipeline and visualization. This tutorial describes both the setup of the processing pipeline and usage of the SSVEP-BCI plugin.&lt;br /&gt;
&lt;br /&gt;
== Editing the Processing Pipeline ==&lt;br /&gt;
&lt;br /&gt;
In the following figure an edited setup of the SSVEP-BCI processing pipeline is shown in the MNE-Scan GUI environment. For this example the NoiseReduction plugin was interposed as a preprocessing step in order to increase the Signal-to-Noise ratio. However, the SSVEP-BCI plugin can also be processed with &amp;quot;raw&amp;quot; (unfiltered) data by connecting it directly with the  acquisition plugin.&lt;br /&gt;
&lt;br /&gt;
[[File:MNE-Scan processing pipelilne.jpeg|framed|center|SSVEP BCI processing pipeline in the MNE-Scan environment]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Adjusting the SSVEP-BCI ==&lt;br /&gt;
&lt;br /&gt;
Like already mentioned, in the thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot; different scientific approaches are combined in order to realize this BCI. The adjustment of these features can be done by pressing on the configuration icon:[[File:Configuration.png|20px|link=]] on the SSVEP-BCI's GUI which is depictured in the previous figure. With this command, the configuration GUI is opened which is shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[[File:SSVEPConfigurationWindow.jpg|100px|framed|link=]]&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	<entry>
		<id>http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=297</id>
		<title>Setup SSVEP BCI example</title>
		<link rel="alternate" type="text/html" href="http://wiki.mne-cpp.org/index.php?title=Setup_SSVEP_BCI_example&amp;diff=297"/>
				<updated>2016-10-27T13:00:39Z</updated>
		
		<summary type="html">&lt;p&gt;ViktorKL: /* Adjusting the SSVEP-BCI */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Abstract ==&lt;br /&gt;
&lt;br /&gt;
The SSVEP-BCI plugin beholds a Brain Computer Interface (BCI) using Steady-State-Visual-Evoked-Potentials (SSVEP). It was developed under the Master Thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot;, in which the so called [http://iopscience.iop.org/article/10.1088/1741-2560/8/3/036020/meta Bremen-BCI] was used as a role model in order to setup classification pipeline and visualization. This tutorial describes both the setup of the processing pipeline and usage of the SSVEP-BCI plugin.&lt;br /&gt;
&lt;br /&gt;
== Editing the Processing Pipeline ==&lt;br /&gt;
&lt;br /&gt;
In the following figure an edited setup of the SSVEP-BCI processing pipeline is shown in the MNE-Scan GUI environment. For this example the NoiseReduction plugin was interposed as a preprocessing step in order to increase the Signal-to-Noise ratio. However, the SSVEP-BCI plugin can also be processed with &amp;quot;raw&amp;quot; (unfiltered) data by connecting it directly with the  acquisition plugin.&lt;br /&gt;
&lt;br /&gt;
[[File:MNE-Scan processing pipelilne.jpeg|framed|center|SSVEP BCI processing pipeline in the MNE-Scan environment]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Adjusting the SSVEP-BCI ==&lt;br /&gt;
&lt;br /&gt;
Like already mentioned, in the thesis &amp;quot;[http://opac.lbs-ilmenau.gbv.de/DB=1/SET=2/TTL=1/CMD?ACT=SRCHA&amp;amp;IKT=1016&amp;amp;SRT=YOP&amp;amp;TRM=Development+of+a+BCI+based+on+real-time+neural+source+localization Development of a BCI based on real-time neural source localization]&amp;quot; different scientific approaches are combined in order to realize this BCI. The adjustment of these features can be done by pressing on the configuration icon:[[File:Configuration.png|20px|link=]] on the SSVEP-BCI's GUI which is depictured in the previous figure. With this command, the configuration GUI is opened which is shown in the following figure.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[[File:SSVEPConfigurationWindow.jpg|100px|framed|center|Configuration GUI of the SSVEP-BCI plugin]]&lt;/div&gt;</summary>
		<author><name>ViktorKL</name></author>	</entry>

	</feed>