FREQUENCY-DOMAIN DISCRIMINANT-ANALYSIS OF THE ELECTROENCEPHALOGRAM

A frequency domain generalization of the classical quadratic discrimin ant function was applied to the problem of classifying alpha-band mult ichannel electroencephalogram recordings in three task conditions. The data consisted of 41-channel recordings obtained in eyes closed, verb al, and spatial task conditions. Classifier performance was measured b y deriving a decision rule from a training sample of 42 recordings and then applying the obtained rule to a test sample of 46 recordings. Th e proportion of correct classification was .93 in the training sample and .85 in the test sample. The classifier performed better when based on the complete cross-spectral matrix than when restricted to power s pectrum variables. Classification based on a subset of 16 leads reduce d the overall proportion of correct classification to .79 in the train ing sample and to .70 in the test sample.