Pattern recognition of human brain electrical potentials

Abstract
Since brain electrical potentials (BEPs) are correlated with a variety of behavioral and clinical variables, especially tight experimental designs are necessary. Primary analysis, which usually consists of spectral analysis, linear prediction, or zero-cross detection, should match the time scale and dynamics of the states or processes being investigated. Nonneural contaminants must be removed from BEPs prior to computation of summary features. Principal components analysis, ad hoc methods, and stepwise discriminant analysis have been used to extract independent, intuitively appealing, and good-classifying features, respectively. Most pattern classification algorithms have been applied to BEPs including decision functions, trainable classification networks, distance functions, syntactic methods, and hybrids of the preceding. Because of its wide availability, most studies have used stepwise linear discriminant analysis.

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