On the classification of single evoked potentials using a quadratic classifier.

It has been shown that using a computationally simple technique it is possible to classify single event-related potentials associated with upper and lower visual field stimulation with a high degree of accuracy. A flow chart of the algorithm and the FORTRAN 77 computer program required to perform the classification just described are included in the Appendix. The program as given in the Appendix is for a 2 class problem only. Memory limitations (33000 bytes 16-bit words) per user in the time-shared system utilized limits the amount of data that may be in core at any one time. A 2 class, 5 step problem takes approximately 5 min in a PDP ll/45 System (Digital Equipment Corp.).

Signal processing in evoked potential research: averaging and modeling.

Three separate topics are covered in this review. The first deals with the technique of signal averaging. The concept of ensemble averaging is explored, and alternatives to this traditional tool are considered such as crosscorrelation averaging, latency corrected averaging, median averaging, etc. Different measures of variability of single evoked potentials are finally discussed. The second topic deals with modeling of the evoked potentials. The direct and inverse problems of source localization are discussed. Recent results of the application of these techniques to single evoked potentials are given. The third topic deals with the use of principal components for signal representation and comparison. Geometric consideration and varimax rotation of coefficients are discussed and examples given.

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.

High frequency components in the spectrum of the visual evoked potential.

The occurrence of short term high frequency components in the spectra of the VEP is usually overshadowed by larger amplitude slow waves which occur simultaneously with the high frequency waves. The high frequency components become evident when the time varying power spectra of short duration (250 ms) segments of the VEP are examined. Two types of short duration time varying power spectra are considered: one is obtained directly from overlapping time segments of the average VEP; and, the other is obtained by computing the spectra from the average autocorrelation functions of overlapping time segments of the data following each stimulus. The first yields the time varying properties for the spectra of the stimulus-synchronized data while, the latter shows the time varying properties of both the synchronized and unsynchronized data present.