Selective averaging of cognitive

This work is about the development of an alternative way of averaging evoked potentials (EP) of cognitive activities. Since the main assumption of invariant waveforms time locked to the elict-ing events does not hold for cognitive EPs, averaging results in distorted estimates. Our alternative selective averaging nds similar subsequences of xed length with variable latency which are common to all EPs by transforming the mulivariate time series to discrete sequences via vector quantization and applying a sequence alignment algorithm. The method yields a signiicant improvement over common averaging in terms of noise attenuation and is shown to be valid by comparison with results for random data. Results for EP data obtained during a spatial imagination task are reported.