A Generic Approach for Identification of Event Related Brain Potentials via a Competitive Neural Network Structure

We present a novel generic approach to the problem of Event Related Potential identification and classification, based on a competitive N eu(cid:173) ral 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(cid:173) session variable signal patterns are automatically identified, dismiss(cid:173) ing the strong and limiting requirement of a-priori stimulus-related selective grouping of the recorded data.