Train The Classifier.mxs

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			<Text>Feature vector containing signal
during stimulated periods.</Text>
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			<Text>Feature vector containing signal
during stimulated periods.</Text>
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			<Text>&lt;b&gt;Signal processing&lt;/b&gt;

Each frequency is treated separately. There are two important
boxes in the processing chain :

&lt;b&gt;Temporal filter&lt;/b&gt; extracts a single frequency band. It
should be configured by a file generated by the configuration
scenario.

&lt;b&gt;Target Separator&lt;/b&gt; separates targets used to mark one
frequency from the others and sends stimulations to corresponding
outputs (Target/Non-Target). </Text>
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			<Text>Signal processing for the &lt;b&gt;first&lt;/b&gt; stimulation frequency</Text>
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					<Value>336</Value>
				</Attribute>
			</Attributes>
		</Comment>
		<Comment>
			<Identifier>(0x351d028a, 0x0bb16598)</Identifier>
			<Text>Feature vector containing signal
during non-stimulated periods.</Text>
			<Attributes>
				<Attribute>
					<Identifier>(0x473d9a43, 0x97fc0a97)</Identifier>
					<Value>320</Value>
				</Attribute>
				<Attribute>
					<Identifier>(0x7234b86b, 0x2b8651a5)</Identifier>
					<Value>960</Value>
				</Attribute>
			</Attributes>
		</Comment>
		<Comment>
			<Identifier>(0x3d893b1d, 0x5b4c4cb3)</Identifier>
			<Text>&lt;b&gt;Feature extraction&lt;/b&gt;

Finally the classifier receives values of amplitude for
stimulated and non-stimulated periods and trains to distinguish
the two.

One classifier is trained per each stimulation frequency.</Text>
			<Attributes>
				<Attribute>
					<Identifier>(0x473d9a43, 0x97fc0a97)</Identifier>
					<Value>256</Value>
				</Attribute>
				<Attribute>
					<Identifier>(0x7234b86b, 0x2b8651a5)</Identifier>
					<Value>1280</Value>
				</Attribute>
			</Attributes>
		</Comment>
		<Comment>
			<Identifier>(0x49ea97ad, 0x543800c1)</Identifier>
			<Text>Feature vector containing signal
during non-stimulated periods.</Text>
			<Attributes>
				<Attribute>
					<Identifier>(0x473d9a43, 0x97fc0a97)</Identifier>
					<Value>1184</Value>
				</Attribute>
				<Attribute>
					<Identifier>(0x7234b86b, 0x2b8651a5)</Identifier>
					<Value>960</Value>
				</Attribute>
			</Attributes>
		</Comment>
		<Comment>
			<Identifier>(0x57661f1a, 0x63af67d4)</Identifier>
			<Text>Signal processing for the &lt;b&gt;second&lt;/b&gt; stimulation frequency</Text>
			<Attributes>
				<Attribute>
					<Identifier>(0x473d9a43, 0x97fc0a97)</Identifier>
					<Value>1008</Value>
				</Attribute>
				<Attribute>
					<Identifier>(0x7234b86b, 0x2b8651a5)</Identifier>
					<Value>336</Value>
				</Attribute>
			</Attributes>
		</Comment>
		<Comment>
			<Identifier>(0x57c833e1, 0x71ddf32c)</Identifier>
			<Text>&lt;big&gt;&lt;b&gt;Scenario description&lt;/b&gt;&lt;/big&gt;

This step will use the signal recorded during the previous step
&lt;i&gt;training acquisition&lt;/i&gt; to generate classifiers which are in
turn usable in online BCI applications.

&lt;b&gt;&lt;span color="red"&gt;Important :&lt;/span&gt;&lt;/b&gt; Do not forget to
change the settings in the &lt;i&gt;Generic stream reader&lt;/i&gt; to load
the file you recorded in the previous step. By default the
scenario uses example data provided with OpenViBE.

&lt;b&gt;Notice :&lt;/b&gt; this scenario will work for three stimulation
frequencies.  If you wish to use more than three commands in your
applications, you will have to modify it.  This modification is
pretty straightforward just duplicate a branch of the processing
chain and change the files used by boxes &lt;i&gt;Temporal filter&lt;/i&gt;, 
&lt;i&gt;Target Separator&lt;/i&gt; and &lt;i&gt;Classifier trainer&lt;/i&gt;</Text>
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				<Attribute>
					<Identifier>(0x473d9a43, 0x97fc0a97)</Identifier>
					<Value>688</Value>
				</Attribute>
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					<Identifier>(0x7234b86b, 0x2b8651a5)</Identifier>
					<Value>-176</Value>
				</Attribute>
			</Attributes>
		</Comment>
	</Comments>
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