Separated channel convolutional neural network to realize the training free motor imagery BCI systems. (March 2019)

• Record Type: Journal Article
• Authors: Zhu, Xuyang; Li, Peiyang; Li, Cunbo; Yao, Dezhong; Zhang, Rui; Xu, Peng
• Is Part Of: Biomedical signal processing and control. Volume 49(2019)
• Page Start: 396
• Page End: 403
• Publication Date: 2019-03
• Subjects: Brain-computer interface -- Electroencephalography -- Training free -- Deep learning -- Common space pattern; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.12.027
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: The end-to-end deep learning framework to realize the training free motor imagery BCI is proposed. Instead of log-energy from CSP filter, the multi-channels series in CSP data extracted from EEG are adopted as input. A separated channel convolutional network, called SCCN, is proposed to encode the multi-channels EEG. Abstract: In the recent context of Brain-computer interface (BCI), it has been widely known that transferring the knowledge of existing subjects to a new subject can effectively alleviate the extra training burden of BCI users. In this paper, we introduce an end-to-end deep learning framework to realize the training free motor imagery (MI) BCI systems. Specifically, we employ the common space pattern (CSP) extracted from electroencephalography (EEG) as the handcrafted feature. Instead of log-energy, we use the multi-channel series in CSP space to retain the temporal information. Then we propose a separated channel convolutional network, here termed SCCN, to encode the multi-channel data. Finally, the encoded features are concatenated and fed into a recognition network to perform the final MI task recognition. We compared the results of the deep model with classical machine learning algorithms, such as k-nearest neighbors (KNN), logistics regression (LR), linear discriminant analysis (LDA), and support vector machine (SVM). Moreover, the quantitative analysis was evaluated on our dataset and the BCI competition IV-2b dataset. The results have shown that our proposed model can improve the accuracy of EEG based MI classification (2–13% improvement for our dataset and 2–15% improvement for BCI competition IV-2b dataset) in comparison with traditional methods under the training free condition.