Characterization of four-class motor imagery EEG data for the BCI-competition 2005.

Full Abstract

To determine and compare the performance of different classifiers applied to four-class EEG data is the goal of this communication. The EEG data were recorded with 60 electrodes from five subjects performing four different motor-imagery tasks. The EEG signal was modeled by an adaptive autoregressive (AAR) process whose parameters were extracted by Kalman filtering. By these AAR parameters four classifiers were obtained, namely minimum distance analysis (MDA)--for single-channel analysis, and linear discriminant analysis (LDA), k-nearest-neighbor (kNN) classifiers as well as support vector machine (SVM) classifiers for multi-channel analysis. The performance of all four classifiers was quantified and evaluated by Cohen's kappa coefficient, an advantageous measure we introduced here to BCI research for the first time. The single-channel results gave rise to topographic maps that revealed the channels with the highest level of separability between classes for each subject. Our results of the multi-channel analysis indicate SVM as the most successful classifier, whereas kNN performed worst.

Author information

Author/s: Schlögl, Alois (A); Lee, Felix (F); Bischof, Horst (H); Pfurtscheller, Gert (G);

Affiliation: Institute of Human-Computer Interfaces, University of Technology Graz, Krenngasse 37, A-8010 Graz, Austria. alois.schloegl(-atsign-)tugraz.at

Journal and publication information

Publication Type: Comparative Study; Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't

Journal: Journal of neural engineering (J Neural Eng), published in England. (Language: eng)

Reference: 2005-Dec; vol 2 (issue 4) : pp L14-22

Dates: Created 2005/11/30; Completed 2006/04/18; Revised 2006/11/15;

PMID: 16317224, status: MEDLINE (last retrieval date: 2/18/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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