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This article is part of the series Trends in Brain Computer Interfaces.

Open Access Research Article

A Time-Frequency Approach to Feature Extraction for a Brain-Computer Interface with a Comparative Analysis of Performance Measures

Damien Coyle*, Girijesh Prasad and TM McGinnity

Author Affiliations

Intelligent Systems Engineering Laboratory, School of Computing and Intelligent Systems, Faculty of Engineering, University of Ulster, Magee Campus, Derry BT48 7JL, UK

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EURASIP Journal on Advances in Signal Processing 2005, 2005:861614  doi:10.1155/ASP.2005.3141

The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2005/19/861614


Received:2 February 2004
Revisions received:4 October 2004
Published:17 November 2005

© 2005 Coyle et al.

The paper presents an investigation into a time-frequency (TF) method for extracting features from the electroencephalogram (EEG) recorded from subjects performing imagination of left- and right-hand movements. The feature extraction procedure (FEP) extracts frequency domain information to form features whilst time-frequency resolution is attained by localising the fast Fourier transformations (FFTs) of the signals to specific windows localised in time. All features are extracted at the rate of the signal sampling interval from a main feature extraction (FE) window through which all data passes. Subject-specific frequency bands are selected for optimal feature extraction and intraclass variations are reduced by smoothing the spectra for each signal by an interpolation (IP) process. The TF features are classified using linear discriminant analysis (LDA). The FE window has potential advantages for the FEP to be applied in an online brain-computer interface (BCI). The approach achieves good performance when quantified by classification accuracy (CA) rate, information transfer (IT) rate, and mutual information (MI). The information that these performance measures provide about a BCI system is analysed and the importance of this is demonstrated through the results.

Keywords:
brain-computer interface; neuromuscular disorders; electroencephalogram; time-frequency methods; linear classification

Research Article