Signal processing in evoked potential research: applications of filtering and pattern recognition

The separate but closely related topics of waveform estimation by filtering and information extraction by pattern recognition are covered in this review. Because of the low signal-to-noise ratio generally encountered in evoked potential research, a variety of filtering methods have been employed for improving waveform estimation. Initially the filtering was done with analog devices but with the availability of high performance minicomputers virtually all filtering is now done digitally. Filters of various types are considered. Among them are single and multiple channel Wiener filtering, Kalman filtering, minimum mean square error filtering, maximum signal-to-noise filtering, and several types of nonlinear filters. The application of adaptive filtering techniques is also considered. In recent years there has been a continual increase in the application of pattern recognition techniques to the processing of evoked potentials. The techniques are based on statistical decision theory and the underlying basis of these procedures is reviewed. The technique of linear stepwise discriminant analysis is considered as well as the use of general discriminant functions of linear and quadratic types. Applications of these procedures to psychophysiological testing are discussed with particular emphasis on auditory and visual event-related potentials.