Abstract
A Brain Computer Interface (BCI) speller allows human-beings to directly spell characters using eye-gazes, thereby building communication between the human brain and a computer. Convolutional Neural Networks (CNNs) have shown better ability than traditional machine learning methods to increase the character spelling accuracy for the BCI speller. Unfortunately, current CNNs can not learn well the features related to the target signal of the BCI speller. This issue limits these CNNs from further character spelling accuracy improvements. To address this issue, we propose a network, which combines our proposed two CNNs, with an existing CNN. These three CNNs of our network extract different features related to the target BCI signal. Our network uses the ensemble of the features extracted by these CNNs for BCI character spelling. Experimental results on three benchmark datasets show that our network outperforms other methods in most cases, with a significant spelling accuracy improvement up to 38.72%. In addition, the communication speed of the P300 speller based on our network is up to 2.56 times faster than the communication speed of the P300 speller based on other methods.
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Notes
- 1.
In this paper, we use “raw data, information, or signals” to denote the data which are only preprocessed (e.g., bandpass filtering and normalization) but not abstracted by a feature extraction method (e.g., a CNN).
References
Blankertz, B.: BCI competition II (2003). http://www.bbci.de/competition/ii/
Blankertz, B.: BCI competition III (2008). http://www.bbci.de/competition/iii/
Bonnet, L., Lotte, F., Lécuyer, A.: Two brains, one game: design and evaluation of a multiuser bci video game based on motor imagery. IEEE Trans. Comput. Intell. AI Games 5(2), 185–198 (2013)
Bostanov, V.: BCI competition 2003-data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram. IEEE Trans. Biomed. Eng. 51(6), 1057–1061 (2004)
Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT 2010, pp. 177–186. Physica-Verlag, Heidelberg (2010). https://doi.org/10.1007/978-3-7908-2604-3_16
Cecotti, H., Graser, A.: Convolutional neural networks for P300 detection with application to brain-computer interfaces. IEEE Trans. Pattern Anal. Mach. Intell. 33(3), 433–445 (2011)
Chollet, F., et al.: Keras (2015). https://github.com/keras-team/keras
De Boer, P.T., Kroese, D.P., Mannor, S., Rubinstein, R.Y.: A tutorial on the cross-entropy method. Ann. Oper. Res. 134(1), 19–67 (2005)
Faux, S.F., Torello, M.W., McCarley, R.W., Shenton, M.E., Duffy, F.H.: P300 in schizophrenia: confirmation and statistical validation of temporal region deficit in P300 topography. Biol. Psychiatry 23(8), 776–790 (1988)
Fazel-Rezai, R., Allison, B.Z., Guger, C., Sellers, E.W., Kleih, S.C., Kübler, A.: P300 brain computer interface: current challenges and emerging trends. Front. Neuroeng. 5, 14 (2012)
Hoffmann, U., Vesin, J.M., Ebrahimi, T.: Spatial filters for the classification of event-related potentials, Technical report (2006)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Lin, C.T., Lin, B.S., et al.: Brain computer interface-based smart living environmental auto-adjustment control system in UPnP home networking. IEEE Syst. J. 8(2), 363–370 (2014)
Lin, C.T., Tsai, S.F., Ko, L.W.: EEG-based learning system for online motion sickness level estimation in a dynamic vehicle environment. IEEE Trans. Neural Netw. Learn. Syst. 24(10), 1689–1700 (2013)
Liu, M., Wu, W., Gu, Z., Yu, Z., Qi, F., Li, Y.: Deep learning based on batch normalization for P300 signal detection. Neurocomputing 275, 288–297 (2018)
Manor, R., Geva, A.B.: Convolutional neural network for multi-category rapid serial visual presentation BCI. Front. Comput. Neurosci. 9, 146 (2015)
Mennes, M., Wouters, H., Vanrumste, B., Lagae, L., Stiers, P.: Validation of ICA as a tool to remove eye movement artifacts from EEG/ERP. Psychophysiology 47(6), 1142–1150 (2010)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807–814 (2010)
Pires, G., Nunes, U., Castelo-Branco, M.: Statistical spatial filtering for a P300-based BCI: tests in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis. J. Neurosci. Methods 195(2), 270–281 (2011)
Polich, J.: Updating P300: an integrative theory of P3a and P3b. Clin. Neurophysiol. 118(10), 2128–2148 (2007)
Rakotomamonjy, A., Guigue, V.: BCI competition III: dataset II-ensemble of SVMs for BCI P300 speller. IEEE Trans. Biomed. Eng. 55(3), 1147–1154 (2008)
Rivet, B., Souloumiac, A., et al.: xDAWN algorithm to enhance evoked potentials: application to brain-computer interface. IEEE Trans. Biomed. Eng. 56(8), 2035–2043 (2009)
Shan, H., Liu, Y., Stefanov, T.: A simple convolutional neural network for accurate P300 detection and character spelling in brain computer interface. In: 27th International Joint Conference on Artificial Intelligence (IJCAI), pp. 1604–1610 (2018)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Wolpaw, J., Wolpaw, E.W.: Brain-Computer Interfaces: Principles and Practice. OUP, Oxford (2012)
Wolpaw, J.R., Ramoser, H., McFarland, D.J., Pfurtscheller, G.: EEG-based communication: improved accuracy by response verification. IEEE Trans. Rehabil. Eng. 6(3), 326–333 (1998)
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Shan, H., Liu, Y., Stefanov, T. (2019). Ensemble of Convolutional Neural Networks for P300 Speller in Brain Computer Interface. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science(), vol 11730. Springer, Cham. https://doi.org/10.1007/978-3-030-30490-4_31
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