Feature extraction for on-line EEG classification using principal components and linear discriminants.

Abstract:

:The study focuses on the problems of dimensionality reduction by means of principal component analysis (PCA) in the context of single-trial EEG data classification (i.e. discriminating between imagined left- and right-hand movement). The principal components with the highest variance, however, do not necessarily carry the greatest information to enable a discrimination between classes. An EEG data set is presented where principal components with high variance cannot be used for discrimination. In addition, a method based on linear discriminant analysis (LDA), is introduced that detects principal components which can be used for discrimination, leading to data sets of reduced dimensionality but similar classification accuracy.

journal_name

Med Biol Eng Comput

authors

Lugger K,Flotzinger D,Schlögl A,Pregenzer M,Pfurtscheller G

doi

10.1007/BF02522476

subject

Has Abstract

pub_date

1998-05-01 00:00:00

pages

309-14

issue

3

eissn

0140-0118

issn

1741-0444

journal_volume

36

pub_type

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