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			<subfield code="a">CONF</subfield>
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			<subfield code="a">galan:2007:iccn/IDIAP</subfield>
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		<datafield tag="245" ind1=" " ind2=" ">
			<subfield code="a">Visuo-Spatial Attention Frame Recognition for Brain-Computer Interfaces</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Galán, Ferran</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Palix, J.</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Chavarriaga, Ricardo</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Ferrez, Pierre W.</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Lew, Eileen</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Hauert, C. -A.</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Millán, José del R.</subfield>
		</datafield>
		<datafield tag="856" ind1="4" ind2="0">
			<subfield code="i">EXTERNAL</subfield>
			<subfield code="u">http://publications.idiap.ch/attachments/reports/2007/galan_2007_iccn.pdf</subfield>
			<subfield code="x">PUBLIC</subfield>
		</datafield>
		<datafield tag="711" ind1="2" ind2=" ">
			<subfield code="a">Proceedings of the 1st International Conference on Cognitive Neurodynamics</subfield>
		</datafield>
		<datafield tag="260" ind1=" " ind2=" ">
			<subfield code="c">2007</subfield>
			<subfield code="a">Shanghai, China</subfield>
		</datafield>
		<datafield tag="771" ind1="2" ind2=" ">
			<subfield code="d">November 2007</subfield>
		</datafield>
		<datafield tag="520" ind1=" " ind2=" ">
			<subfield code="a">Objective: To assess the feasibility of recognizing visual spatial attention frames for Brain-computer interfaces (BCI) applications. Methods: EEG data was recorded with 64 electrodes from 2 subjects executing a visual spatial attention task indicating 2 target locations. Continuous Morlet wavelet coefficients were estimated on 18 frequency components and 16 preselected electrodes in trials of 600 ms. The spatial patterns of the 16 frequency components frames were simultaneously detected and classified (between the two targets). The classification accuracy was assessed using 20-fold crossvalidation. Results: The maximum frames average classification accuracies are 80.64% and 87.31% for subject 1 and 2 respectively, both utilizing coefficients estimated at frequencies located in gamma band.</subfield>
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