Designing Spectral and Spatial Filters Simultaneously for Brain-Computer Interface Based on Mutual Information and Particle Swarm Optimization

Foodeh, Reza; Shalchyan, Vahid; Daliri, Mohammad Reza

doi:10.22041/ijbme.2017.72918.1271

Document Type : Full Research Paper

Authors

¹ Ph.D Student, Biomedical Engineering Department, Electrical Engineering Faculty, Iran university of Science and Technology, Tehran, Iran

² Assistant Professor, Biomedical Engineering Department, Electrical Engineering Faculty, Iran university of Science and Technology, Tehran, Iran

³ Associate Professor, Biomedical Engineering Department, Electrical Engineering Faculty, Iran university of Science and Technology

https://doi.org/10.22041/ijbme.2017.72918.1271

Abstract

Extracting discriminative features is a crucial step in brain-computer interfaces (BCIs) that could affect directly on the classification performance. Common spatial patterns (CSP) is a commonly used algorithm for such propose in motor imagery based BCI systems. CPS tries to extract the most appropriate spatial patterns in the electroencephalogram (EEG) signals to discriminate different motor imagery classes. Before applying CSP, Usually EEG signals are filtered out in 8-30 Hz to capture event related desynchronization (ERD) specific frequency rhythms called mu and beta bands. However, this frequency band could be highly subject specific. Therefore, optimizing spectral and spatial filters jointly could improve the classification accuracy. In this paper, we proposed a novel learning algorithm to derive spatial and spectral filters simultaneously using an evolutionary learning algorithm called particle swarm optimization (PSO). Furthermore, we utilized mutual information between extracted features and class labels as a cost function in the learning algorithm. Our simulations on BCI competition IV, dataset 1 reveals that the proposed method significantly outperforms the conventional CSP and filter bank CSP (FBCSP) with two different filter bank architectures.

Keywords

References

[1] J. Meng, L. Yao, X. Sheng, D. Zhang, and X. Zhu, “Simultaneously optimizing spatial spectral features based on mutual information for EEG classification,” IEEE Transactions on Biomedical Engineering, vol. 62, no. 1, pp. 227-240, 2015.

[2] 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.

[3] J. Wolpaw, and E. W. Wolpaw, Brain-computer interfaces: principles and practice: Oxford University Press, 2012.

[4] H. Zhang, Z. Y. Chin, K. K. Ang, C. Guan, and C. Wang, “Optimum spatio-spectral filtering network for brain–computer interface,” IEEE Transactions on Neural Networks, vol. 22, no. 1, pp. 52-63, 2011.

[5] S. Lemm, B. Blankertz, G. Curio, and K.-R. Muller, “Spatio-spectral filters for improving the classification of single trial EEG,” IEEE Transactions on Biomedical Engineering, vol. 52, no. 9, pp. 1541-1548, 2005.

[6] G. Dornhege, B. Blankertz, M. Krauledat, F. Losch, G. Curio, and K.-R. Muller, “Combined optimization of spatial and temporal filters for improving brain-computer interfacing,” IEEE transactions on biomedical engineering, vol. 53, no. 11, pp. 2274-2281, 2006.

[7] K. K. Ang, Z. Y. Chin, H. Zhang, and C. Guan, "Filter bank common spatial pattern (FBCSP) in brain-computer interface." pp. 2390-2397.

[8] R. Tomioka, G. Dornhege, G. Nolte, B. Blankertz, K. Aihara, and K.-R. Müller, “Spectrally weighted common spatial pattern algorithm for single trial EEG classification,” Dept. Math. Eng., Univ. Tokyo, Tokyo, Japan, Tech. Rep, vol. 40, 2006.

[9] B. Blankertz, G. Dornhege, M. Krauledat, K.-R. Müller, and G. Curio, “The non-invasive Berlin brain–computer interface: fast acquisition of effective performance in untrained subjects,” NeuroImage, vol. 37, no. 2, pp. 539-550, 2007.

[10] G. H. Klem, H. O. Lüders, H. Jasper, and C. Elger, “The ten-twenty electrode system of the International Federation,” Electroencephalogr Clin Neurophysiol, vol. 52, no. suppl., pp. 3, 1999.

[11] H. Ramoser, J. Muller-Gerking, and G. Pfurtscheller, “Optimal spatial filtering of single trial EEG during imagined hand movement,” Rehabilitation Engineering, IEEE Transactions on, vol. 8, no. 4, pp. 441-446, 2000.

[12] G. Strang, G. Strang, G. Strang, and G. Strang, Introduction to linear algebra: Wellesley-Cambridge Press Wellesley, MA, 1993.

[13] K. K. Ang, Z. Y. Chin, C. Wang, C. Guan, and H. Zhang, “Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b,” Frontiers in neuroscience, vol. 6, 2012.

[14] J. Kennedy, "Particle swarm optimization," Encyclopedia of machine learning, pp. 760-766: Springer, 2011.

[15] G. Pfurtscheller, and C. Neuper, “Motor imagery and direct brain-computer communication,” Proceedings of the IEEE, vol. 89, no. 7, pp. 1123-1134, 2001.

[16] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification: John Wiley & Sons, 2012.

Iranian Journal of Biomedical Engineering

Designing Spectral and Spatial Filters Simultaneously for Brain-Computer Interface Based on Mutual Information and Particle Swarm Optimization

References

References

Volume 10, Issue 3
October 2016
Pages 267-277

Designing Spectral and Spatial Filters Simultaneously for Brain-Computer Interface Based on Mutual Information and Particle Swarm Optimization

References

References

Volume 10, Issue 3October 2016Pages 267-277

Volume 10, Issue 3
October 2016
Pages 267-277