Automated analysis of the electrical activity of the human brain (EEG): A progress report

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Abstract
Clinical evaluation of electroencephalographic (EEG) recordings is based on complex subjective processes of data reduction and feature extraction. The high dimensionality of the EEG signal, its variability, and the lack of standard population values have retarded development of automated systems. An interactive, real-time analysis system (ADIEEG) has been implemented to develop features to simplify visual interpretation and facilitate automated classification. It uses a 40 000 word PDP15-PDP11 dual processor computer. Resident code occupies approximately 11 000 locations, while a maximum of 12 000 locations are used for buffers. The system performs 1) continuous spectral analysis using the fast Fourier transform to produce estimates of power and coherence, 2) parallel time domain analysis to detect sharp transients significant to diagnosis, 3) several forms of graphics, 4) simple algorithms to reject noncortical and instrumental artifact, 5) interactive parameter alteration and on-line feedback to adjust decision thresholds when necessary, and 6) extraction of diagnostically helpful features using heuristics based on clinical EEG. The ADIEEG system resides in the University of California, San Francisco Medical Center, and Langley Porter Neuropsychiatric Institute.

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