Abstract
The application is reviewed of neural-network (NN) signal processing methods to neurological waveform detection and pattern analysis. NN methods are shown to be an excellent way of incorporating expert knowledge about the brain into a mathematical framework with minimal assumptions about the statistics of signal and noise. Constrained by expert knowledge, NN algorithms can search for optimal and near-optimal connections between, and weightings of, application specific features in data spaces for which human knowledge is incomplete. Applying NN algorithms to electrical signals noninvasively recorded from the human brain, the neurological effects of different types of sleeping pills have been differentiated, and insights have been gained as to how our brains produce higher cognitive functions.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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