Study of on-line adaptive discriminant analysis for EEG-based brain computer interfaces.

Published

Journal Article

A study of different on-line adaptive classifiers, using various feature types is presented. Motor imagery brain computer interface (BCI) experiments were carried out with 18 naive able-bodied subjects. Experiments were done with three two-class, cue-based, electroencephalogram (EEG)-based systems. Two continuously adaptive classifiers were tested: adaptive quadratic and linear discriminant analysis. Three feature types were analyzed, adaptive autoregressive parameters, logarithmic band power estimates and the concatenation of both. Results show that all systems are stable and that the concatenation of features with continuously adaptive linear discriminant analysis classifier is the best choice of all. Also, a comparison of the latter with a discontinuously updated linear discriminant analysis, carried out in on-line experiments with six subjects, showed that on-line adaptation performed significantly better than a discontinuous update. Finally a static subject-specific baseline was also provided and used to compare performance measurements of both types of adaptation.

Full Text

Duke Authors

Cited Authors

  • Vidaurre, C; Schlögl, A; Cabeza, R; Scherer, R; Pfurtscheller, G

Published Date

  • March 2007

Published In

Volume / Issue

  • 54 / 3

Start / End Page

  • 550 - 556

PubMed ID

  • 17355071

Pubmed Central ID

  • 17355071

Electronic International Standard Serial Number (EISSN)

  • 1558-2531

International Standard Serial Number (ISSN)

  • 0018-9294

Digital Object Identifier (DOI)

  • 10.1109/tbme.2006.888836

Language

  • eng