Abstract
Classification of electroencephalography (EEG) signals is a fundamental issue of Brain Computer Interface (BCI) systems, and deep learning techniques are still under investigation although they are dominant in other fields like computer vision and natural language processing. In this paper, we introduce the chessboard image transformation method in which the motor imagery EEG signals were transformed into images in order to be classified using a hybrid deep learning model. The EEG motor movement/imagery Physionet dataset was used and the Motor Imagery (MI) signals for two frequency bands (Mu [8–13 Hz] and Beta [13–30 Hz]) were transformed into 2-channel images (one channel for each band). The network model consists of Deep Convolutional Neural Network (DCNN) to extract the spatial and frequency features followed by Long Short Term Memory (LSTM) to extract temporal features and then finally to be classified into 5 different classes (4 motor imagery tasks and one rest). The results were promising with 68.72% classification accuracy for the chessboard approach compared to 68.13% for the azimuthal projection with Clough-Tocher interpolation (2-bands scenario) and to 64.64% average accuracy for a baseline method, i.e., Support Vector Machine (SVM).
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References
Chuanqi, T., Fuchun, S., Big, F., Tao, K., Wenchang, Z.: Autoencoder-based transfer learning in brain–computer interface for rehabilitation robot. Int. J. Adv. Robot. Syst. 16(2), 1729881419840860 (2019)
Youngjoo, K., Jiwoo, R., Ko, K.K., Clive, C.T., Danilo, P.M., Cheolsoo, P.: Motor imagery classification using mu and beta rhythms of EEG with strong uncorrelating transform based complex common spatial patterns. Comput. Intell. Neurosci. 2016, 13 p. (2016). Hindawi Publishing Corporation
McFarland, D.J., Wolpaw, J.R.: EEG-based brain-computer interfaces. Curr. opin. Biomed. Eng. 4, 194–200 (2017)
Abiri, R., Borhani, S., Sellers, E., Jiang, Y., Zhao, X.: A comprehensive review of EEG-based brain-computer interface paradigms. J. Neural Eng. 16, 011001 (2019)
Hsu, W.-Y.: EEG-based motor imagery classification using enhanced active segment selection and adaptive classifier. Comput. Biol. Med. 41(8), 633–639 (2011)
Song, X., Yoon, S.-C., Perera, V.: Adaptive common spatial pattern for single-trial EEG classification in multisubject BCI. In: International IEEE/EMBS Conference on Neural Engineering (NER), pp. 411–414. IEEE (2013)
Llera, A., Gomez, V., Kappen, H.J.: Adaptive multiclass classification for brain computer interfaces. Neural Comput. 26(6), 1108–1127 (2014)
Barachant, A., Bonnet, S., Congedo, M., Jutten, C.: Multi-class brain computer interface classification by Riemannian geometry. IEEE Trans. Biomed. Eng. 59(4), 920–928 (2012)
Congedo, M., Barachant, A., Kharati, K.: Classification of covariance matrices using a Riemannian-based kernel for BCI applications. IEEE Trans. Signal Process. 65, 2211–2220 (2016)
Phan, A.-H., Cichocki, A.: Tensor decompositions for feature extraction and classification of high dimensional datasets. Nonlinear Theory and its Applications (NOLTA) IEICE 1(1), 37–68 (2010)
Maitreyee, W.: Motor Imagery based Brain Computer Interface (BCI) using Artificial Neural Networks Classifiers. University of Reading (2016)
Qiao, W., Bi, X.: Deep spatial-temporal neural network for classification of EEG-based motor imagery. In: Proceedings of the 2019 International Conference on Artificial Intelligence and Computer Science, pp. 265–272 (2019)
Tabar, Y., Halici, U.: A novel deep learning approach for classification of EEG motor imagery signals. J. Neural Eng. 14, 016003 (2017)
Xu, B., et al.: Wavelet transform time-frequency image and convolutional network-based motor imagery EEG classification. IEEE Access 7, 6084–6093 (2018)
Chaudhary, S., Taran, S., Bajaj, V., Sengur, A.: Convolutional neural network based approach towards motor imagery tasks EEG signals classification. IEEE Sens. J. 19(12), 4494–4500 (2019)
Ha, K.W., Jeong, J.W.: Motor imagery EEG classification using capsule networks. Sensors 19, 2854 (2019)
Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51(6), 1034–1043 (2004)
Loboda, A., Margineanu, A., Rotariu, G., Lazar, A.M.: Discrimination of EEG-based motor imagery tasks by means of a simple phase information method. Int. J. Adv. Res. Artif. Intell. 3(10), 11–15 (2014)
Fadel, W., Kollod, C., Wahdow, M., Ibrahim, Y., Ulbert, I.: Multi-class classification of motor imagery EEG signals using image-based deep recurrent convolutional neural network. In: 2020 8th International Winter Conference on Brain-Computer Interface (BCI), Gangwon, Korea (South), pp. 1–4. IEEE (2020)
LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time-series. In: Arbib, M. (ed.) The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge (1995)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR, pp. 1–14 (2015)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Acknowledgment
This work was supported by the Hungarian National Research Development and Innovation Office, Thematic Excellence Program, NKFIH-848-8/2019, National Brain Research Program, 2017-1.2.1-NKP-2017-00002, National Bionics Program ED_17-1-2017-0009.
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Fadel, W., Wahdow, M., Kollod, C., Marton, G., Ulbert, I. (2020). Chessboard EEG Images Classification for BCI Systems Using Deep Neural Network. In: Chen, Y., Nakano, T., Lin, L., Mahfuz, M., Guo, W. (eds) Bio-inspired Information and Communication Technologies. BICT 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 329. Springer, Cham. https://doi.org/10.1007/978-3-030-57115-3_8
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