Research on the Key Technologies of Motor Imagery EEG Signal Based on Deep Learning

Journal: Journal of Autonomous Intelligence DOI: 10.32629/jai.v2i4.60

Zhuozheng Wang, Zhuo Ma, Xiuwen Du, Yingjie Dong, Wei Liu

Beijing University of Technology

Abstract

Brain-computer interface (BCI) is an emerging area of research that establishes a connection between the brain and external devices in a completely new way. It provides a new idea about the rehabilitation of brain diseases, human-computer interaction and augmented reality. One of the main problems of implementing BCI is to recognize and classify the motor imagery Electroencephalography(EEG) signals effectively. This paper takes the characteristic data of motor imagery of EEG as the research object to conduct the research of multi-classification method. In this study, we use the Emotiv helmet with 16 biomedical sensors to obtain EEG signal, adopt the fast independent component analysis and the fast Fourier transform to realize signal preprocessing and select the common spatial pattern algorithm to extract the features of the motor imagery EEG signal. In order to improve the accuracy of recognition of EEG signal, a new deep learning network is designed for multi-channel self-acquired EEG data set which is named as min-VGG-LSTMnet in this paper. This network combines Long Short-Term Memory Network with convolutional neural network VGG and achieves the four-classification task of the left-hand, right-hand, left-foot and right-foot lifting movements based on motor imagery. The results show that the accuracy of the proposed classification method is at least 8.18% higher than other mainstream deep-learning methods.

Keywords

Electroencephalography; Motor Imagery; Convolutional Neural Network; Long Short-term Memory Network

References

[1]. Zied Tayeb; Juri Fedjaev; Nejla Ghaboosi; et al. Validating deep neural networks for online decoding of motor imagery movements from EEG signals. Sensors 2019; 19(1): 210.
[2]. JJ Shih; Krusienski; Dean J; et al. Brain-computer interfaces in medicine. Mayo Clinic Proceedings 2012; 87: 268–279.
[3]. Pfurtscheller G. Functional brain imaging based on ERD/ERS. Vision Research 2001; 41(10-11): 1257-1260.
[4]. Obermaier B; Neuper C. Information transfer rate in a five-classes brain-computer interface. IEEE Trans Neural Syst Rehabil Eng 2001; 9(3): 283-288.
[5]. Wan B; Liu Y. Multi-pattern motor imagery recognition based on EEG features. Journal of Tianjin University 2010; 43(10): 895-900.
[6]. Xu X; Wang N. Feature extraction and classification of EEG signals in four kinds of motion imagination. Journal of Nanjing University of Posts and Telecommunications (Social Science) 2017; 37(06): 18-22.
[7]. Blankertz B; Müller KR. The BCI competition 2003. IEEE Transactions on Biomedical Engineering 2004; 51(6): 1044-1051.
[8]. Michel Cotsaftis. The autonomous intelligence challenge. Journal of Autonomous Intelligence 2018; 1(1): 1-1.
[9]. Manu Mitra. Neural processor in artificial intelligence advancement. Journal of Autonomous Intelligence 2018; 1(1): 2-14.
[10]. Delorme A; Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 2004; 134(1): 9-21.
[11]. Ahmad A; Xavier J. 3D to 2D bijection for spherical objects under equidistant fisheye projection. Computer Vision and Image Understanding 2014; 125: 172-183.
[12]. Jiaqing Chen; Xiaohui Mu; Yinglei Song; et al. Flame recognition in video images with color and dynamic features of flames. Journal of Autonomous Intelligence 2019; 1(1): 11-29.
[13]. Weide Li, Juan Zhang. An innovated integrated model using singular spectrum analysis and support vector regression optimized by intelligent algorithm for rainfall forecasting. Journal of Autonomous Intelligence 2019; 1(1): 30-45.
[14]. Krizhevsky A; Sutskever I. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 2012; 1097-1105.
[15]. Cho K; Van Merriënboer B. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv 2014; 1406.
[16]. Graves A; Fernández S. Bidirectional LSTM networks for improved phoneme classification and recognition. International Conference on Artificial Neural Networks 2005; 799-804.
[17]. Lawhern VJ; Solon AJ. EEGNet: A compact convolutional neural network for EEG-based brain – computer interfaces. Journal of Neural Engineering 2018; 15(5): 056013.
[18]. Ordóñez F; Roggen D. Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 2016; 16: 115.
[19]. Yao S; Hu S; Zhao Y; et al. Deepsense: A unified deep learning framework for time-series mobile sensing data processing. In Proceedings of the 26th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee 2017; 351–360.
[20]. Okita T; Inoue S. Activity recognition: Translation across sensor modalities using deep learning. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers 2018; 1462–1471.
[21]. Ha K-W; Jeong J-W. Motor imagery EEG classification using capsule networks. Sensors 2019; 19: 2854.
[22]. Nicolas-Alonso; Luis F; Gomez-Gil J. Brain computer interfaces, a review. Sensors 2012; 12: 1211–1279.
[23]. Kim D-W; Lee J-C; Park Y-M; et al. Auditory brain-computer interfaces (BCIs) and their practical applications. Biomedical Engineering Letters 2012; 2: 13–17.
[24]. Michel Cotsaftis. Autonomous intelligence: An advance level in modern technology. Journal of Autonomous Intelligence 2018; 1(1): 44-44.
[25]. Yongzhong Lu; Min Zhou; Shiping Chen; et al. A perspective of conventional and bioinspired optimization techniques in maximum likelihood parameter estimation. Journal of Autonomous Intelligence 2018; 2(1): 1-12.
[26]. Lotte F; Bougrain L; Cichocki A; et al. A review of classification algorithms for EEG-based brain-com -puter interfaces: A 10-year update. Journal of Neural Engineering 2018; 15: 031005.
[27]. Park J; Min K; Kim H; et al. Road surface classification using a deep ensemble network with sensor feature selection. Sensors 2018; 18: 4342.
[28]. Zhao R; Yan R; Wang J; et al. A hybrid CNN – LSTM algorithm for online defect recognition of CO2 welding. Sensors (Basel) 2017; 17(2).
[29]. Gao M; Shi G; Li S. Online prediction of ship behavior with automatic identification system sensor data using bidirectional long short-term memory recurrent neural network. Sensors 2018; 18: 4211.
[30]. Liu C; Wang Y; Kumar K; et al. Investigations on speaker adaptation of LSTM RNN models for speech recognition. In Proceedings of the International Conference on Acoustics, Speech and Signal Processing 2016; 5020–5024.

Copyright © 2020 Zhuozheng Wang, Zhuo Ma, Xiuwen Du, Yingjie Dong, Wei Liu

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