Heuristic real time feature extraction of the electroencephalogram (EEG)

Abstract

The extremely complex nature of the electroencephalogram (EEG), and the subtle, nonquantified methods of pattern recognition used by human interpreters have made EEG analysis resistant to automation. Attempts at pattern recognition using multivariate classification procedures have not produced generalizable results due to the inadequate degree and quality of feature extraction prior to classification. A real time, on-line EEG analysis strategy is described which incorporates feature extracting algorithms derived from models of human EEG interpretation. A system based upon this strategy has been implemented on a dedicated minicomputer. It includes: 1) spectral analysis using the Fast Fourier Transform (FFT) to produce continuous estimates of power and coherence; 2) parallel time domain analysis to detect the occurrence of sharp transient events of possible clinical significance; 3) continuous isometric display of spectral and transient functions; 4) spectral and time domain algorithms for the rejection of noncortical and instrumental artifact; 5) heuristics to isolate patterns and events of potential clinical significance; 6) interactive alteration of analysis and display parameters to facilitate manipulation of data from various experimental paradigms; 7) on-line feedback to alter, when necessary, artifact rejection, transient detection and feature extraction decision thresholds.