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			<subfield code="a">ARTICLE</subfield>
		</datafield>
		<datafield tag="970" ind1=" " ind2=" ">
			<subfield code="a">Garipelli_IEEETRANS-BIOMED-ENGG_2008/IDIAP</subfield>
		</datafield>
		<datafield tag="245" ind1=" " ind2=" ">
			<subfield code="a">Fast Recognition of Anticipation Related Potentials</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Garipelli, Gangadhar</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Chavarriaga, Ricardo</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Millán, José del R.</subfield>
		</datafield>
		<datafield tag="653" ind1="1" ind2=" ">
			<subfield code="a">Anticipation</subfield>
		</datafield>
		<datafield tag="653" ind1="1" ind2=" ">
			<subfield code="a">brain-computer interaction (BCI)</subfield>
		</datafield>
		<datafield tag="653" ind1="1" ind2=" ">
			<subfield code="a">contingent
negative variation (CNV)</subfield>
		</datafield>
		<datafield tag="653" ind1="1" ind2=" ">
			<subfield code="a">electroencephalogram (EEG)</subfield>
		</datafield>
		<datafield tag="856" ind1="4" ind2="0">
			<subfield code="i">EXTERNAL</subfield>
			<subfield code="u">http://publications.idiap.ch/attachments/papers/2008/Garipelli_IEEETRANS-BIOMED-ENGG_2008.pdf</subfield>
			<subfield code="x">PUBLIC</subfield>
		</datafield>
		<datafield tag="773" ind1=" " ind2=" ">
			<subfield code="p">IEEE Transactions on Biomedical Engineering</subfield>
		</datafield>
		<datafield tag="260" ind1=" " ind2=" ">
			<subfield code="c">2008</subfield>
		</datafield>
		<datafield tag="500" ind1=" " ind2=" ">
			<subfield code="a">In press</subfield>
		</datafield>
		<datafield tag="520" ind1=" " ind2=" ">
			<subfield code="a">Anticipation increases the efficiency of daily tasks by partial
advance activation of neural substrates involved in it. Here we
develop a method for the recognition of
electroencephalogram (EEG) correlates of this activation as early
as possible on single trials which is essential for Brain-Computer
Interaction (BCI). We explore various features from the EEG
recorded in a Contingent Negative Variation (CNV) paradigm. We
also develop a novel technique called Time Aggregation of
Classification (TAC) for fast and reliable decisions that combines
the posterior probabilities of several classifiers trained with
features computed from temporal blocks of EEG
until a certainty threshold is reached. Experiments with 9 naive
subjects performing the CNV experiment with GO and NOGO conditions
with an inter-stimulus interval of 4 s show that the performance
of the TAC method is above 70% for four subjects, around 60% for
two other subjects, and random for the remaining subjects. On
average over all subjects, more than 50\% of the correct decisions
are made at 2 s, without needing to wait until 4 s.</subfield>
		</datafield>
	</record>
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