Multi-channel piecewise selective averaging of cognitive evoked potentials with variable latency

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 eliciting events does not hold for cognitive EPs, averaging results in distorted estimates. The authors' alternative selective averaging finds similar subsequences of fixed length with variable latency which are common to all multi-channel EPs by transforming the multivariate time series to discrete sequences via vector quantization and applying a sequence alignment algorithm. The method yields a significant 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.