In this paper, we develop a new type of brain-computer interface (BCI) which is able to control a computer game by motor imagery electroencephalogram (EEG). We propose a new framework of feature extractions using common spatial frequency patterns (CSFP) for classification of motor imagery EEG. The aim of our BCI system is to provide an on-line “hit rat” game control with short response time and subject-specific adaptation of system parameters. Our BCI system is able to detect three different motor imagery-related brain patterns (imagination of limb movements: left hand, right hand and both feet) from the ongoing brain activity by using only five EEG channels. The best hit accuracy of the game with fast response time attained by subject 2 is about 73%, which demonstrates that our BCI system has the ability of providing much fast BCI of even 1 s per command.
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References
Birbaumer, N., Ghanayim, N., Hinterberger, T, et al. (1999) A spelling device for paralysed. Nature 398, 297-298.
Pfurtscheller, G., Flotzinger, D., Kalcher, J. (1993) Brain-computer interface: a new communication device for handicapped persons. Journal of Microcomputer Applications 16 (3), 293-299.
Vaughan, T., Wolpaw, J., Donchin, E. (1996) EEG-based communication: prospects and problems. IEEE Trans. on Neural Systems and Rehabilitation (4), 425-430.
Blankertz, B., Curio, G., Muller, K.-R. (2002) Classifying single trial EEG: towards brain computer interfacing. Advances in Neural Information Processing Systems 14, 157-164.
Wolpaw, J., Birbaumer, N., et al. (2000) Brain-computer interface technology: a review of the first international meeting 8 (2), 164-173.
Wolpaw, J.R., Birbaumer, N., et al. (2002) Brain-computer interfaces for communication and control. Clinical Neurophysiology 113, 767-791.
M üller, K.-R., Krauledat, M., Dornhege, G., Curio, G., Blankertz, B. (2007) Machine learning and applications for brain-computer Interfacing, in M.J. Smith, G. Salvendy (Eds.): Human Interface, Part I, HCII 2007, Springer LNCS 4557, pp. 705-714.
Pfurtscheller, G., Miiller-Putz, G., Graimann, B., Scherer, R., Schlogl, A., Vidaurre, C., Wriessnegger, S., Neuper, C. (2007) Graz-brain-computer interface: state of research. Toward Brain-Computer Interfacing.
Li, Y., Gao, X., Liu, H., Gao, S. (2004) Classification of single-trial electroencephalogram during finger movement. Biomedical Engineering, IEEE Transactions on 51 (6), 1019-1025.
Wang, Y., Zhang, Z., Li, Y., Gao, X., Gao, S., Yang, F. (2004) BCI competition 2003-data set IV: an algorithm based on CSSD and FDA for classifying single-trial EEG. Biomedical Engineering, IEEE Transactions on 51 (6), 1081-1086.
Muller-Gerking, J., Pfurtscheller, G., Flyvbjerg, H. (1999) Designing optimal spatial filters for single-trial EEG classification in a movement task. Clinical Neurophysiology 110, 787-798.
Pfurtscheller, G., Brunner, C., Schlogl, A., Lopes da Silva, F. (2006) Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage 31 (1), 153-159.
Smola, A., Gretton, A., Borgwardt, K., Bedo, J. (2007) Supervised feature selection via dependence estimation. Proceedings of the 24th international conference on Machine learning, 823-830.
Ince, N., Tewfik, A., Arica, S. (2007) Extraction subject-specific motor imagery time- frequency patterns for single trial EEG classification. Computers in Biology and Medicine 37 (4),499-508.
Kalkan, H., Ince, F., Tewfik, A., Yardimci, Y., Pearson, T. (2007) Extraction of optimal timefrequency plane features for classification. Signal Processing and Communications Applications, 2007. SIU 2007. IEEE 15th, 1-4.
Yang, B., Yan, G., Wu, T., Yan, R. (2007) Subject-based feature extraction using fuzzy wavelet packet in brain-computer interfaces. Signal Processing 87 (7), 1569-1574.
Blankertz, B., Dornhege, G., Krauledat, M., M üller, K.R., Curio, G. (2007) The non-invasive Berlin brain-computer interface: fast acquisition of effective performance in untrained subjects. Neuroimage 37 (2) 539-550.
Krepki, R., Blankertz, B., Curio, G., M üller, K.R. (2007) The Berlin brain-computer interface (BBCI) - towards a new communication channel for online control in gaming applications. Multimedia Tools and Applications 33 (1), 73-90.
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Zhao, Q., Zhang, L., Li, J. (2008). Multi-Task BCI for Online Game Control. In: Mahr, B., Huanye, S. (eds) Autonomous Systems – Self-Organization, Management, and Control. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8889-6_4
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DOI: https://doi.org/10.1007/978-1-4020-8889-6_4
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