Recognition of EEG Based on Stacked Sparse Denoising Auto-Encoder

被引：0
作者：
Tang X.-L. [1 ]
Liu Y.-W. [1 ]
Wang Y.-L. [1 ]
Ma Y.-W. [1 ]
机构：
[1] College of Automation, Chongqing University of Posts and Telecommunications, Nan'an, Chongqing
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2019年 / 48卷 / 01期
关键词：
Deep learning; Denoising auto-encoder; EEG recognition; Sparse; Stack;
D O I：
10.3969/j.issn.1001-0548.2019.01.011
中图分类号：
学科分类号：
摘要：
An improved algorithm, stacked sparse denoising auto-encoder (SSDAE), is proposed in this paper. In the new algorithm, the noise of original input data is processed, the hidden layers is limited to sparse restrictions, finally, EEG features are classified with the softmax. Experiments results on two different data sets (Laboratory data sets and 2005 BCI competition data set IVa) demonstrate that SSDAE had the highest recognition accuracy than traditional algorithms, which proves that SSDAE has the stronger learning ability and robustness in motor imagery EEG feature extraction. © 2019, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.
引用
收藏
页码：62 / 67
页数：5
相关论文
共 17 条
[1]  
Bodda S., Chandranpillai H., Viswam P., Et al., Categorizing imagined right and left motor imagery BCI tasks for low-cost robotic neuroprosthesis, International Conference on Electronics, and Optimization Techniques, pp. 3670-3673, (2016)
[2]  
Sun H.-W., Fu Y.-F., Xiong X., Identification of EEG induced by motor imagery based on Hilbert-Huang Transform, Acta Automatica Sinica, 41, 9, pp. 1686-1692, (2015)
[3]  
Das A.K., Suresh S., Sundararajan N., A discriminative subject-specific spatio-spectral filter selection approach for EEG based motor-imagery task classification, Expert Systems with Applications, 64, pp. 375-384, (2016)
[4]  
Zhao L., Sun S.-H., Jia Y.-M., Et al., Wavelet transform based distributed information consensus filter, Control and Decision, 1, pp. 45-60, (2016)
[5]  
Aghaei S., Mahanta M.S., Plataniotis K.N., Separable common spatio-spectral patterns for motor imagery BCI systems, IEEE Trans on Biomedical Engineering, 1, pp. 15-29, (2016)
[6]  
Cheng R.-W., Huang R., Research of general expressin for nonlinear auto regressive model and its forecast application, System Engineering Theory and Practice, 35, 9, pp. 2370-2379, (2015)
[7]  
Hendryli J., Fanany M.I., Classifying abnormal activities in exam using multi-class Markov chain LDA based on MODEC features, International Conference on Information and Communication Technology, (2016)
[8]  
Zheng W.L., Lu B.L., Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks, International IEEE/EMBS Conference, pp. 154-157, (2015)
[9]  
Guo Y., Tao D., Yu J., Et al., Deep neural networks with relativity learning for facial expression recognition, IEEE International Conference on Multimedia & Expo workshops, pp. 1-6, (2016)
[10]  
Guo M.-W., Zhao Y.-Z., Xiang J.-P., Et al., Review of object detection methods based on SVM, Control and Decision, 2, pp. 193-200, (2014)
12