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Comparison of decision rules for automatic EEG classification

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2 Author(s)
Yunck, T.P. ; Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA, USA ; Tuteur, F.B.

Discusses eight classification rules, four based on parametric Gaussian assumptions and four based on nonparametric k-nearest neighbor density estimation, which were tested on human EEG samples representing seven forms of mental activity. With a set of primary EEG features, the k-NN rules, as a class, were significantly more effective than the parametric classifiers; best results were obtained with an optimized version of the generalized k-NN rule. With a reduced set of secondary features, the two types performed approximately equally, but below the best k-NN performances in the original space.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:PAMI-2 ,  Issue: 5 )

Date of Publication:

Sept. 1980

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