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Frequency domain discriminant analysis of the electroencephalogram
Author(s) -
LIND JOHN C.,
KOLES ZOLTAN J.,
FLORHENRY PIERRE,
SOONG ANTHONY C. K.
Publication year - 1997
Publication title -
psychophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.661
H-Index - 156
eISSN - 1469-8986
pISSN - 0048-5772
DOI - 10.1111/j.1469-8986.1997.tb02406.x
Subject(s) - quadratic classifier , linear discriminant analysis , pattern recognition (psychology) , psychology , discriminant function analysis , artificial intelligence , discriminant , classifier (uml) , sample (material) , electroencephalography , statistics , generalization , speech recognition , mathematics , computer science , mathematical analysis , chemistry , chromatography , psychiatry
A frequency domain generalization of the classical quadratic discriminant function was applied to the problem of classifying alpha‐band multichannel electroencephalogram recordings in three task conditions. The data consisted of 41‐channel recordings obtained in eyes closed, verbal, and spatial task conditions. Classifier performance was measured by deriving a decision rule from a training sample of 42 recordings and then applying the obtained rule to a test sample of 46 recordings The 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 spectrum variables Classification based on a subset of 16 leads reduced the overall proportion of correct classification to 79 in the training sample and to 70 in the test sample.