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 Computjournal_title
Medical & biological engineering & computingauthors
Lugger K,Flotzinger D,Schlögl A,Pregenzer M,Pfurtscheller Gdoi
10.1007/BF02522476subject
Has Abstractpub_date
1998-05-01 00:00:00pages
309-14issue
3eissn
0140-0118issn
1741-0444journal_volume
36pub_type
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