A Time-Frequency Approach to Feature Extraction for a Brain-Computer Interface with a Comparative Analysis of Performanc
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A Time-Frequency Approach to Feature Extraction for a Brain-Computer Interface with a Comparative Analysis of Performance Measures Damien Coyle Intelligent Systems Engineering Laboratory, School of Computing and Intelligent Systems, Faculty of Engineering, University of Ulster, Magee Campus, Derry BT48 7JL, UK Email: [email protected]
Girijesh Prasad Intelligent Systems Engineering Laboratory, School of Computing and Intelligent Systems, Faculty of Engineering, University of Ulster, Magee Campus, Derry BT48 7JL, UK Email: [email protected]
T. M. McGinnity Intelligent Systems Engineering Laboratory, School of Computing and Intelligent Systems, Faculty of Engineering, University of Ulster, Magee Campus, Derry BT48 7JL, UK Email: [email protected] Received 2 February 2004; Revised 4 October 2004 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 and phrases: brain-computer interface, neuromuscular disorders, electroencephalogram, time-frequency methods, linear classification.
1.
INTRODUCTION
Nearly two million people in the United States [1] are affected by neuromuscular disorders. A conservative estimate of the overall prevalence is that 1 in 3500 of the world’s population may be expected to have a disabling inherited neuromuscular disorder presenting in childhood or in later life [2]. In many cases those affected may have no control over muscles that would normally be used for communication. BCI technology is a developing technology but has the potential to contribute to the improvement of living standards for these people by offering an alternative communication channel which does not depend on the peripheral nerves or muscles [3]. A BCI replaces the use of nerves and muscles
and the movements they produce with electrophysiological signals in conjunction with the hardware and softwa
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