CLASSIFICATION OF MOTOR IMAGINARY TASKS USING ADAPTIVE RECURSIVE BANDPASS FILTER - Effective Classification for Motor Imaginary BCI

Vickneswaran Jeyabalan, Andrews Samraj, Loo Chu Kiong

2008

Abstract

The noteworthy point in the advancement of Brain Computer Interface (BCI) research is not only to develop a new technology but also to adopt the easiest procedures since the expected beneficiaries are of disabled. The nature of the locked-in patients is that, they possess strong mental ability in thinking and understanding but they are extremely unable to express their views. Imagination is possible for almost all of the locked-in patients; hence a BCI which does not rely on finger movements or other muscle activity is definitely an added advantage in this arena. The objective of this paper is to identify and classify motor imaginary signals extracted from the left and right cortex of the human brain. This is realised by implementing an adaptive bandpass filter with the combination of frequency shifting and segmentation techniques. The signals are captured using Electro-Encephalogram (EEG) from the C3, C4, and Cz channels of the scalp electrodes and is pre-processed to expose the motor imaginary signals. The result of classification using a simple threshold articulates the effectiveness of our proposed technique. The best results were found in the latency range of 3 to 9 seconds of the imagination and this proves the existing neuro-science knowledge.

References

  1. Baharan Kamousi, Zhongming Liu, and Bin He, 2005, Classification of Motor Imaginary Tasks for BrainComputer Interface Applications by Means of Two Equivalent Dipoles Analysis, IEEE Trans.Neural Sys.and Rehab. Engineering,Vol 13.
  2. BCI Competition II, 2003 http://ida.first.fraunhofer.de/ projects/bci/competition_ii
  3. D.Coyle, G.Prasad, and T.M.McGinnity, 2005, A TimeFrequency Approach to Feature Extraction for BrainComputer Interface with a Comparative Analysis of Performance Measures, EURASIP Journal on Applied Signal Processing,Hindawi publishing corp.
  4. E.Donchin,K.M.Spencer, and R.Wijesinghe,Themental prosthesis:Assesing the speed of a P300-based brain computer interface,IEEE Trans.Rehab.Eng.,Vol 8,pp.174-179.
  5. G. Pfurtsheller and C. Neuper, 2003, Movement and ERD/ERS, Kluwer Academic/Plenum Publishers, Dordrecht, pp. 191-207.
  6. J. Kalcher and G. Pfurtscheller, 1995, “Discrimination between phase-locked and nonphase-locked eventrelated EEG activity,” Electroenceph. Clin. Neurophysiol, vol. 94, pp. 381-4.
  7. Lemm, S.; Schafer, C. and Curio, G., 2004,BCI competition 2003-data set III: probabilistic modeling of sensorimotor mu- rhythms for classification of imaginary hand movements,Biomedical Engineering, IEEE Transactions Vol 51, pp.1077 - 1080.
  8. R. R. Gharieb and A. Cichocki, 2001, Segmentation and tracking of the electro-encephalogram signal using an adaptive recursive bandpass filter, Medical & Biological Engineering & Computing 2001, Vol. 39, pp. 237-248.
  9. R. R. Gharieb and A. Cichocki, 2001, On-line EEG classification and sleep spindles detection using an adaptive recursive bandpass filter, Acoustics, Speech, and Signal Processing, Proceedings. (ICASSP'01) IEEE International Conference Vol 2.
  10. R.V. Raja Kumar and R.N. Pal, 1985,A gradient algorithm for center-frequency adaptive recursive band pass filters, Proc. IEEE Vol 73,pp.371-372.
  11. R.V. Raja Kumar and R.N. Pal, 1990, Tracking of bandpass signals using center-frequency adaptive filters, IEEE Trans. Acoust. Speech signal processing., vol 38, pp.1710-1721.
  12. S.Andrews, R.Palaniappan, and N.Kamel, 2005, Extracting Single Trial Visual Evoked Potentials using Selective Eigen-Rate Principal Components, Proceed. of World Academy of Science, Engineeering. and Tech., Vol 7.
  13. S.Andrews, T.Andrew and C.H.Loo, 2007, Inquisitive Impact of Different Electrode Configuration in Representing Single Trial P300 Visual Event Related Potentials,,College Science in India.,Vol 2007.
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Paper Citation


in Harvard Style

Jeyabalan V., Samraj A. and Chu Kiong L. (2008). CLASSIFICATION OF MOTOR IMAGINARY TASKS USING ADAPTIVE RECURSIVE BANDPASS FILTER - Effective Classification for Motor Imaginary BCI . In Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2008) ISBN 978-989-8111-60-9, pages 113-118. DOI: 10.5220/0001935501130118


in Bibtex Style

@conference{sigmap08,
author={Vickneswaran Jeyabalan and Andrews Samraj and Loo Chu Kiong},
title={CLASSIFICATION OF MOTOR IMAGINARY TASKS USING ADAPTIVE RECURSIVE BANDPASS FILTER - Effective Classification for Motor Imaginary BCI},
booktitle={Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2008)},
year={2008},
pages={113-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001935501130118},
isbn={978-989-8111-60-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2008)
TI - CLASSIFICATION OF MOTOR IMAGINARY TASKS USING ADAPTIVE RECURSIVE BANDPASS FILTER - Effective Classification for Motor Imaginary BCI
SN - 978-989-8111-60-9
AU - Jeyabalan V.
AU - Samraj A.
AU - Chu Kiong L.
PY - 2008
SP - 113
EP - 118
DO - 10.5220/0001935501130118