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Abstract:
We present a novel generic approach to the problem of Event
Related Potential identification and classification, based on a
competitive Neural Net architecture. The network weights converge
to the embedded signal patterns, resulting in the formation of a
matched filter bank. The network performance is analyzed via a
simulation study, exploring identification robustness under low SNR
conditions and compared to the expected performance from an
information theoretic perspective. The classifier is applied to
real event-related potential data recorded during a classic
odd-ball type paradigm; for the first time, within-session variable
signal patterns are automatically identified, dismissing the strong
and limiting requirement of a-priori stimulus-related selective
grouping of the recorded data. The results present new
possibilities in evoked potential research.
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