Abstract
Motor imagery based Brain Computer Interface (BCI) system is a promising strategy for the rehabilitation of stroke patients. Common Spatial Pattern (CSP) is frequently used in feature extraction of motor imagery EEG signals and its performance depends heavily on the choice of frequency component. Moreover, EEG of stroke patients, which is full of noise, makes it hard for traditional CSP to extract discriminative patterns for classification. In order to deal with the subject-specific band selection, in this paper, we adopt denoising autoencoders and contractive autoencoders to extract and compose robust features from CSP features filtered in multiple frequency bands. We compare our method with traditional methods on data collected from two months clinical rehabilitation. The results not only demonstrate its superior recognition performance but also evidence the effectiveness of our BCI-FES rehabilitation training system.
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References
Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain–computer interfaces for communication and control. Clinical Neurophysiology 113(6), 767–791 (2002)
Pfurtscheller, G., Müller-Putz, G.R., Pfurtscheller, J., Rupp, R.: Eeg-based asynchronous bci controls functional electrical stimulation in a tetraplegic patient. EURASIP Journal on Applied Signal Processing 2005, 3152–3155 (2005)
Ramoser, H., Muller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single trial eeg during imagined hand movement. IEEE Transactions on Rehabilitation Engineering 8(4), 441–446 (2000)
Zhang, H., Zhang, L.: Spatial-spectral boosting analysis for stroke patients’ motor imagery eeg in rehabilitation training. CoRR, Vol. abs/1310.6288 (2013)
Li, J., Zhang, L.: Regularized tensor discriminant analysis for single trial eeg classification in bci. Pattern Recognition Letters 31(7), 619–628 (2010)
Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proceedings of the IEEE 89(7), 1123–1134 (2001)
Song, L., Gordon, E., Gysels, E.: Phase synchrony rate for the recognition of motor imagery in brain-computer interface. Advances in Neural Information Processing Systems 18, 1265 (2006)
Shahid, S., Sinha, R.K., Prasad, G.: Mu and beta rhythm modulations in motor imagery related post-stroke eeg: a study under bci framework for post-stroke rehabilitation. BMC Neuroscience 11(Suppl. 1), P127 (2010)
Liang, J., Zhang, H., Liu, Y., Wang, H., Li, J., Zhang, L.: A frequency boosting method for motor imagery EEG classification in BCI-FES rehabilitation training system. In: Guo, C., Hou, Z.-G., Zeng, Z. (eds.) ISNN 2013, Part II. LNCS, vol. 7952, pp. 284–291. Springer, Heidelberg (2013)
Wang, H., Liu, Y., Zhang, H., Li, J., Zhang, L.: Causal neurofeedback based BCI-FES rehabilitation for post-stroke patients. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013. LNCS, vol. 8226, pp. 419–426. Springer, Heidelberg (2013)
Liu, Y., Li, M., Zhang, H., Li, J., Jia, J., Wu, Y., Cao, J., Zhang, L.: Single-trial discrimination of eeg signals for stroke patients: A general multi-way analysis. In: EMBC 2013, pp. 2204–2207. IEEE (2013)
Vincent, P., et al.: Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. The Journal of Machine Learning Research 9999, 3371–3408 (2010)
Rifai, S., Vincent, P., Muller, X., Glorot, X., Bengio, Y.: Contractive auto-encoders: Explicit invariance during feature extraction. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 833–840 (2011)
Liu, Y., Zhang, H., Wang, H., Li, J., Zhang, L.: Bci-fes rehabilitation training platform integrated with active training mechanism. In: IJCAI 2013 Workshop on Intelligence Science (2013)
Li, J., Zhang, L.: Active training paradigm for motor imagery bci. Experimental brain research 219(2), 245–254 (2012)
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.-A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine learning, pp. 1096–1103. ACM (2008)
Garson, G.D.: Interpreting neural-network connection weights. AI expert 6(4), 46–51 (1991)
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Chen, M., Liu, Y., Zhang, L. (2014). Classification of Stroke Patients’ Motor Imagery EEG with Autoencoders in BCI-FES Rehabilitation Training System. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham. https://doi.org/10.1007/978-3-319-12643-2_25
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DOI: https://doi.org/10.1007/978-3-319-12643-2_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12642-5
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