Design and Implementation of a Real-Time Brain-Computer Interface
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
and it has been the primary means of devising BCIs. The BCI technology has the potential to enable severely disabled people to
drive computers directly by brain activity rather than by physical means. It is specially useful for those who are totally paralysed
or have other severe motor disabilities such as people suffering from motor neurone diseases (MNDs) and spinal cord injury
[1][2]. Nearly two million people in USA alone [2] are affected from neuro-muscular disorders. A conservative estimate of
overall prevalence is that 1 in 3500 of the world population may be expected to have a disabling inherited neuro-muscular
disorder presenting in childhood or in later life [1]. In UK, there are over 5000 patients with some type of neuro-muscular
disorders [4]. In many cases such patients may have no control over any of their muscles. An appropriate BCI technology can
therefore help improve the standard of living for these people. A BCI mainly involves a feature extraction procedure (FEP), a
translation algorithm (TA), and an optional feedback mechanism [5]-[8]. The FEP obtains the dominant discriminating features
from the EEG activity during two or more specific thought processes. The TA translates or classifies these features into a
specific control signal. The optional feedback mechanism helps the user become more efficient in controlling their EEG for
specific tasks (e.g. cursor movement).
A state-of-the-art EEG-based BCI experimental set-up has recently been bought and installed in a custom-built
screened lab at the Intelligent Systems Engineering Centre (ISEC) at Magee campus. Using this setup, the project
would involve planning and conducting experiment on both able-bodied and disabled subjects to record data from
sufficient number of trials. The recorded data is to be used to design a BCI with an appropriate feature vector and a
classifier. The designed BCI will be implemented in the BCI experimental setup and applied for on-line cursor
control. An appropriate feedback mechanism will be incorporated and further extended experimentation will be
carried out to assess the variability in the designed BCI characteristics. An appropriate adaptation mechanism will
then be devised to account for the variability.
Hardware/software systems involved: PC under Windows XP, MATLAB, C/C++.
Bibliography
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locked in”, Psychology Bulletin, 2001, Vol. 127, pp 358-375.
[2] Wolpaw J.R., Birbaumer, N., McFarland DJ, Pfurtscheller, G., Vaughan, TM, “Brain-computer interfaces for
communication and control “, Clinical Neurophysiology, 2002, Vol. 113, pp 767-791.
[3] Emery, A.E.H., “Population frequencies of inherited neuro-muscular diseases – A world survey”, Neuro-muscular
disorders, Vol. 1, No. 1, 1991, pp 19-29.
[4] MNDs: http://news.bbc.co.uk/1/hi/health/1588329.stm.
[5] Pfurtscheller G. , Neuper, C., “Motor imagery and direct brain computer communication”, Proc. IEEE, 2001; Vol. 89(7),
pp 1123-1134.
[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
interface with a comparative analysis of performance measures", (to appear in) Applied Signal Processing, The
International Journal of the European Association for Signal Processing (EURASIP), (special issue - Trends in BrainComputer Interfaces).
[8] Herman, P., Prasad, G., McGinnity T.M. (2005) “Investigation of the Type-2 Fuzzy Logic Approach to Classification in
an EEG-based Brain-Computer Interface”, (accepted in) 27th International IEEE EMBS Conference, Sept., 2005,
Shanghai, China.
[9] Scherer, R, Muller, GR, Neuper C, Graimann, B., Pfurtscheller, G., “An Asynchronously controlled EEG-based virtual
keyboard: Improvement of the spelling rate”, IEEE Trans. on Neural Systems and Rehabilitation Engineering, 2004, Vol.
12, No.6, pp 979-984.
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