ABSTRACT
In Brain Computer interface (BCI) system, motor imagination has some problems, such as difficulty in extracting EEG signal features, low accuracy of classification and recognition, long training time and gradient saturation in feature classification based on traditional deep neural network, etc. In this paper, a deep belief network (DBN) model is proposed. Fast Fourier transform (FFT) and wavelet transform (WT) combined with deep machine learning model DBN were used to extract the feature vectors of time-frequency signals of different leads, superposition and average them, and then perform classification experiments. The number of DBN network layers and the number of neurons in each layer were determined by iteration. Through the reverse fine-tuning, the optimal weight coefficient W and the paranoid term B are determined layer by layer, and the training and optimization problems of deep neural networks are solved. In this paper, a motion imagination and Motion observation (MI-AO) experiment is designed, which can be obtained by comparing with the public dataset BCI Competition IV 2a. The DBN model is used to compare with other algorithms, and the average accuracy of binary classification is 83.81%, and the average accuracy of four classification is 80.77%.
- Bram V D L, Gurkok H, Plass-Oude Bos D, Experiencing BCI Control in a Popular Computer Game[J]. IEEE Transactions on Computational Intelligence and AI in Games,2013, 5(2): 176-184.Google Scholar
- ADAMS M, BEN-SALEM S, ISLAM Z, Towards an SSVEP-BCI controlled smart home [C]// 2019 IEEE International Conference on Systems Man and Cybernetics (SMC). Bari: IEEE, 2019: 2737–2742.Google Scholar
- CHEN L, WANG Z P, HE F, An online hybrid braincomputer interface combining multiple physiological signals for webpage browse [C]// 2015 37th Annual International Conference ofhe IEEE Engineering in Medicine and Biology Society. Milan: IEEE, 2015: 1152–1155.Google Scholar
- WU Jia-ling, GAO Zhong-ke. Brain-computer interface technology and its applications in neuroscience [J]. Chinese Journal of Modern Neurological Diseases, 2021, 21(1): 3–8.Google Scholar
- HUO Shou-jun, HAO Yan, SHI Hui-yu, Pattern recognition of motor imagery EEG based on deep convolutional networks [J]. Computer Applications, 2020, 41(4): 1042–1048.Google Scholar
- Sohn I.Deep Belief Network based Intrusion Detection Techniques:A Survey [J].Expert Systems with Applications,2021,(167):1∼9.Google Scholar
- Jia H P, Liu J, Zhang M, et al.Network Intrusion Detection Based on IE-DBN Model [J].Computer Communications,2021,(178):131∼140.Google Scholar
- Qin Mengxin. Research on optimization of EEG data processing algorithm based on deep belief network [D]. Beijing Jiaotong University,2020.Google Scholar
- CAI Jun, Hu Yangkui, Zhang Yi, Yin Chunlin. Multi-band frequency domain deep belief network eeg feature recognition algorithm [J]. Robot,2018,40(04):510-517.Google Scholar
- Cao Shuai. Research on classification method of eeg signal based on deep learning [D]. South China University of Technology,2017.Google Scholar
- José Escorcia-Gutierrez, Kelvin Beleño, Javier Jimenez-Cabas, Mohamed Elhoseny, Mohammad Dahman Alshehri, Mahmoud M. Selim,An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems,Measurement,Volume 196,2022,111226,ISSN 0263-2241.Google ScholarCross Ref
- Hou Haibo. Deep learning based classification algorithm for motor imagery EEG signals [D]. Harbin Engineering University,2021.Google Scholar
- GAUR P, PACHORI R B, WANG H, A multi-class EEGbased BCI classification using multivariate empirical mode decomposition based filtering and Riemannian Geometry [J]. Expert Systems with Applications, 2018, 95: 201–211.Google ScholarCross Ref
Index Terms
- Comparative Study on EEG Feature Recognition based on Deep Belief Network
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