Abstract
A novel communication channel from brain to machine, the research of Brain-computer interfacing is attracted more and more attention recently. In this paper, a novel method based on Bayesian Network is proposed to analyze multi motor imagery task. On the one hand, the channel physical position and mean motor imagery class information are adopted as constrains in BN structure construction. On the other hand, continuous Gaussian distribution model is used to model the bayesian network nodes other than discretizing variable in traditional methods, which would reflect the real character of EEG signals. Finally, the network structure and edge inference score are used to construct SVM classifier. Experimental results on the BCI competition data and lab collected data show that the average accuracy of the two experiments are 93% and 88%, which are better comparing to current methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blankertz, B., Tangermann, M., Vidau-rre, C., Fazli, S., Sannelli, C., Haufe, S., Maeder, C., Ramsey, L.E., Sturm, I., Curio, G., Müller, K.-R.: The Berlin brain-computer-interface: non-medical uses of BCI technology. Front. Neurosci. 4(198), 10–13 (2010)
Pfurtscheller, G., Neuper, C., Flotzinger, D., Pregenzer, M.: EEG based discrimination between imagination of right and left hand movement. Clin. Neurophysiol. 103, 42–651 (1997)
Lemm, S., Blankertz, B., Curio, G., Müller, K.-R.: Spatio-spectral filters for robust classification of single trial EEG. IEEE Trans. Biomed. Eng. 52, 1541–1548 (2005)
Blankertz, B., Losch, F., Krauledat, M., Dornhege, G., Curio, G., Müller, K.-R.: The Berlin brain-computer interface: accurate performance from first-session in BCI-naïve subjects. IEEE Trans. Biomed. Eng. 55, 2452–2462 (2008a)
Grosse-Wentrup, M., Buss, M.: Multiclass common spatial patterns and information theoretic feature extraction. IEEE Trans. Biomed. Eng. 55, 1991–2000 (2008)
Dornhege, G., Blankertz, B., Curio, G., Müller, K.-R.: Increase information transfer rates in BCI by csp extension to multi-class. Adv. Neural Inf. Process. Syst. 16 (2003)
Dornhege, G., Blankertz, B., Curio, G., Müller, K.-R.: Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms. IEEE Trans. Biomed. Eng. 51, 993–1002 (2004)
Krusienski, D.J., McFarland, D.J., Wolpaw, J.R.: An evaluation of autoregressive spectral estimation model order for brain-computer interface applications. In: IEEE EMBS Ann. Int. Conf., vol. 1, pp. 1323–1326 (2006)
Hinterberger, T., Kubler, A., Kaiser, J.: A brain-computer interface (BCI) for the locked-in: comparison of different EEG classifications for the thought translation device. Clin. Neurophys. 114(3), 416–425 (2003)
Pfurtscheller, G., Kalcher, J., Neuper, C., On-line, E.E.G.: classification during externally-paced hand movements using a neural network-based classifier. Electroenceph. Clin. Neurophys. 99(5), 416–425 (1996)
Geiger, H.: Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. Machine Learning 20(3), 197–243 (1995)
Guosheng, Y., Yingzi, L., Prabir, B.: A driver fatigue recognition model based on information fusion and dynamic Bayesian network. Information Sciences 180(10), 1942–1954 (2010)
Kwang-Eun, K., Hyun-Chang, Y., Kwee-Bo, S.: Emotion Recognition using EEG Signals with Relative Power Values and Bayesian Network. International Journal of Control, Automation and Systems 7(5), 865–870 (2009)
Xiangyang, L., Qiang, J.: Active Affective State Detection and User Assistance With Dynamic Bayesian Networks. IEEE Transactions on Systems, Man and Cybernetics 35(1), 93–105 (2005)
Cao, L., Li, J., Sun, Y., Zhu, H., Yan, C.: EEG-based vigilance analysis by using fisher score and PCA algorithm. In: IEEE Progress in Informatics and Computing (PIC), pp. 175–179 (2005)
Blankertz, B.: BCI Competition III Final Results, http://www.bbci.de/competition/iii/results/#winner (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
He, Lh., Liu, B. (2014). Motor Imagery EEG Signals Analysis Based on Bayesian Network with Gaussian Distribution. In: Huang, DS., Han, K., Gromiha, M. (eds) Intelligent Computing in Bioinformatics. ICIC 2014. Lecture Notes in Computer Science(), vol 8590. Springer, Cham. https://doi.org/10.1007/978-3-319-09330-7_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-09330-7_29
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09329-1
Online ISBN: 978-3-319-09330-7
eBook Packages: Computer ScienceComputer Science (R0)