Extracting Rhythmic Brain Activity for  Brain-Computer Interfacing through Constrained  Independent Component Analysis

Wang, Suogang; James, Christopher J.

doi:https://doi.org/10.1155/2007/41468

Computational Intelligence and Neuroscience

On this page

Abstract References Copyright Related Articles

Special Issue

Brain-Computer Interfaces: Towards Practical Implementations and Potential Applications

View this Special Issue

Research Article | Open Access

Volume 2007 | Article ID 041468 | https://doi.org/10.1155/2007/41468

Extracting Rhythmic Brain Activity for Brain-Computer Interfacing through Constrained Independent Component Analysis

Suogang Wang¹and Christopher J. James¹

Academic Editor: Andrzej Cichocki

Received31 Dec 2006

Accepted18 Jun 2007

Published23 Aug 2007

Abstract

We propose a technique based on independent component analysis (ICA) with constraints, applied to the rhythmic electroencephalographic (EEG) data recorded from a brain-computer interfacing (BCI) system. ICA is a technique that can decompose the recorded EEG into its underlying independent components and in BCI involving motor imagery, the aim is to isolate rhythmic activity over the sensorimotor cortex. We demonstrate that, through the technique of spectrally constrained ICA, we can learn a spatial filter suited to each individual EEG recording. This can effectively extract discriminatory information from two types of single-trial EEG data. Through the use of the ICA algorithm, the classification accuracy is improved by about 25%, on average, compared to the performance on the unpreprocessed data. This implies that this ICA technique can be reliably used to identify and extract BCI-related rhythmic activity underlying the recordings where a particular filter is learned for each subject. The high classification rate and low computational cost make it a promising algorithm for application to an online BCI system.

References

S. Rush and D. A. Driscoll, “EEG electrode sensitivity—an application of reciprocity,” IEEE Transactions on Biomedical Engineering, vol. 16, no. 1, pp. 15–22, 1969.
View at: Google Scholar
J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clinical Neurophysiology, vol. 113, no. 6, pp. 767–791, 2002.
View at: Publisher Site | Google Scholar
J. W. Kozelka and T. A. Pedley, “Beta and mu rhythms,” Journal of Clinical Neurophysiology, vol. 7, no. 2, pp. 191–207, 1990.
View at: Publisher Site | Google Scholar
J. R. Wolpaw, D. J. McFarland, G. W. Neat, and C. A. Forneris, “An EEG-based brain-computer interface for cursor control,” Electroencephalography and Clinical Neurophysiology, vol. 78, no. 3, pp. 252–259, 1991.
View at: Publisher Site | Google Scholar
G. Pfurtscheller, “Graphical display and statistical evaluation of event-related desynchronization (ERD),” Electroencephalography and Clinical Neurophysiology, vol. 43, no. 5, pp. 757–760, 1977.
View at: Publisher Site | Google Scholar
N.-J. Huan and R. Palaniappan, “Classification of mental tasks using fixed and adaptive autoregressive models of EEG signals,” in Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '04), pp. 507–510, San Francisco, Calif, USA, September 2004.
View at: Google Scholar
H. Ramoser, J. Müller-Gerking, and G. Pfurtscheller, “Optimal spatial filtering of single trial EEG during imagined hand movement,” IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 4, pp. 441–446, 2000.
View at: Publisher Site | Google Scholar
A. Hyvärinen and E. Oja, “A fast fixed-point algorithm for independent component analysis,” Neural Computation, vol. 9, no. 7, pp. 1483–1492, 1997.
View at: Google Scholar
A. J. Bell and T. J. Sejnowski, “An information-maximization approach to blind separation and blind deconvolution,” Neural Computation, vol. 7, no. 6, pp. 1129–1159, 1995.
View at: Google Scholar
C. J. James and C. W. Hesse, “A comparison of time structure and statistically based BSS methods in the context of long-term epileptiform EEG recordings,” in Proceedings of the 5th International Conference on Independent Component Analysis and Blind Signal Separation (ICA '04), pp. 1025–1032, Granada, Spain, September 2004.
View at: Google Scholar
O. Gibson and C. J. James, “On the analysis of seizure onset in the EEG: the application of constrained ICA,” in Proceedings of the 2nd European Medical and Biological Engineering Conference (EMBEC '02), pp. 424–426, Vienna, Austria, December 2002.
View at: Google Scholar
A. K. Barros, A. Mansour, and N. Ohnishi, “Removing artifacts from electrocardiographic signals using independent components analysis,” Neurocomputing, vol. 22, no. 1–3, pp. 173–186, 1998.
View at: Publisher Site | Google Scholar
R. Vigário, J. Särelä, V. Jousmäki, M. Hämäläinen, and E. Oja, “Independent component approach to the analysis of EEG and MEG recordings,” IEEE Transactions on Biomedical Engineering, vol. 47, no. 5, pp. 589–593, 2000.
View at: Publisher Site | Google Scholar
M. J. McKeown, T.-P. Jung, S. Makeig et al., “Spatially independent activity patterns in functional MRI data during the Stroop color-naming task,” Proceedings of the National Academy of Sciences of the United States of America, vol. 95, no. 3, pp. 803–810, 1998.
View at: Publisher Site | Google Scholar
N. Xu, X. Gao, B. Hong, X. Miao, S. Gao, and F. Yang, “BCI competition 2003—data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 1067–1072, 2004.
View at: Publisher Site | Google Scholar
S. Wang and C. J. James, “Enhancing evoked responses for BCI through advanced ICA techniques,” in Proceeding of IET 3rd International Conference on Advances in Medical, Signal and Information Processing (MEDSIP '06), p. 38, Glasgow, UK, July 2006.
View at: Publisher Site | Google Scholar
A. Erfani and A. Erfanian, “The effects of mental practice and concentration skills on EEG brain dynamics during motor imagery using independent component analysis,” in Proceedings of the 26th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC '04), vol. 1, pp. 239–242, San Francisco, Calif, USA, September 2004.
View at: Google Scholar
C. J. James and C. W. Hesse, “Independent component analysis for biomedical signals,” Physiological Measurement, vol. 26, no. 1, pp. R15–R39, 2005.
View at: Publisher Site | Google Scholar
C. J. James and O. J. Gibson, “Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 9, pp. 1108–1116, 2003.
View at: Publisher Site | Google Scholar
A. Ziehe and K.-R. Müller, “TDSEP—an efficient algorithm for blind separation using time structure,” in Proceedings of the 8th International Conference on Artificial Neural Networks (ICANN '98), pp. 675–680, Skovde, Sweden, September 1998.
View at: Google Scholar
A. Belouchrani and M. G. Amin, “Blind source separation based on time-frequency signal representations,” IEEE Transactions on Signal Processing, vol. 46, no. 11, pp. 2888–2897, 1998.
View at: Publisher Site | Google Scholar
A. Ziehe, P. Laskov, K.-R. Müeller, and G. Nolte, “A linear least-squares algorithm for joint diagonalization,” in Proceedings of the 4th International Symposium on Independent Component Analysis and Blind Signal Separation, pp. 469–474, Nara, Japan, April 2003.
View at: Google Scholar
C. J. James and C. W. Hesse, “Mapping scalp topographies of rhythmic EEG activity using temporal decorrelation based constrained ICA,” in Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '04), vol. 2, pp. 994–997, San Francisco, Calif, USA, September 2004.
View at: Google Scholar
A. Cichocki, S. Amari, and K. Siwek, “ICALAB toolbox for signal processing,” http://www.bsp.brain.riken.jp/ICALAB/.
View at: Google Scholar
B. Blankertz, K.-R. Müller, G. Curio et al., “The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 1044–1051, 2004.
View at: Publisher Site | Google Scholar
G. Dornhege, B. Blankertz, G. Curio, and K.-R. Müller, “Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 993–1002, 2004.
View at: Publisher Site | Google Scholar
C. J. James and C. W. Hesse, “Observing and tracking brain dynamics in electromagnetic brain signals through blind source separation,” in Proceedings of the IEE Medical Signal and Information Processing Conference (MEDSIP '04), pp. 145–152, Malta, September 2004.
View at: Google Scholar
S. R. Gunn and J. S. Kandola, “Structural modelling with sparse kernels,” Machine Learning, vol. 48, no. 1–3, pp. 137–163, 2002.
View at: Publisher Site | Google Scholar
L. Song and J. Epps, “Classifying EEG for brain-computer interfaces: learning optimal filters for dynamical system features,” in Proceedings of the 23rd International Conference on Machine Learning (ICML '06), pp. 857–864, Pittsburgh, Pa, USA, June 2006.
View at: Google Scholar
C. J. James and S. Wang, “Blind source separation in single-channel EEG analysis: an application to BCI,” in Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '06), pp. 6544–6547, New York, NY, USA, August-September 2006.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2007 Suogang Wang and Christopher J. James. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation Order printed copies

Views

550

Downloads

1151

Citations