Designing a Brain-Computer Interface using support vector machines
A brain-computer interface (BCI) aims to build a communication system that is capable of translating a subject’s intention,
reflected by suitable brain signals, into a control signal. The Electroensephalogram (EEG) reflects brain function as brainwaves.
An EEG-based BCI technology has the potential to enable severely disabled people to drive computers directly by brain activity
rather than by physical means. One of the most effective ways of devising an EEG-based BCI consists of a cognitive task-based
strategy based on scalp EEG recorded over motor cortex area. In this, features are obtained from the EEG data recorded during
a cognitive task imagery [5]-[8]. Assuming a Gaussian feature distribution, linear discreminant analysis (LDA) is normally
used for feature classification. Such an assumption may not always hold resulting in larger a classification error.
It is aimed in this project to investigate the application of support vector machines (SVMs) for devising a BCI classifier. This is
because SVMs do not require making any specific assumption about data distribution. Additionally an SVM-based classifier
design approach involves structural risk minimisation resulting in a robust classifier performance. Making use of standard datasets available in open literature, BCIs will be developed off-line using both LDA and SVM approaches to classifier design. In
order to ascertain the superiority of SVM approach, a comparative performance evaluation will be undertaken by applying both
types of BCIs on data-sets not used in the designing of classifiers. For real-time on-line performance evaluation, designed BCIs
would also be implemented on BCI experimental set-up recently bought and installed in a custom-built screened lab at the
Intelligent Systems Engineering Centre (ISEC) at Magee campus.
Hardware/software systems involved: PC under Windows XP, MATLAB, C/C++.
Bibliography
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[4] MNDs: http://news.bbc.co.uk/1/hi/health/1588329.stm.
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[6] Coyle, D.H., Prasad, G., McGinnity T.M. (2005), “A Time-Series Prediction Approach to Extracting Features for a BrainComputer Interface”, (to appear in) IEEE Trans. On Neural systems and rehabilitation engineering.
[7] Coyle, D.H., Prasad, G., McGinnity T.M. (2005), “A time-frequency approach to feature extraction for a brain-computer
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Shanghai, China.
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