A deep neural network with subdomain adaptation for motor imagery brain-computer interface. (October 2021)

• Record Type: Journal Article
• Authors: Zheng, Minmin; Yang, Banghua
• Is Part Of: Medical engineering & physics. Volume 96(2021)
• Page Start: 29
• Page End: 40
• Publication Date: 2021-10
• Subjects: Motor imagery (MI) -- Transfer learning -- Local maximum mean discrepancy (LMMD) -- Distance within each class (DWC) -- Distance between classes within each domain (DBCWD); Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2021.08.006
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: We introduced an adaptive layer into the full connection layer of a deep convolution neural network. The goal of the adaptive layer is to minimize the intra-class distance and maximize the inter-class distance. The results revealed that the performance of the proposed algorithm was better than that of other algorithms. Abstract: Background: The nonstationarity problem of EEG is very serious, especially for spontaneous signals, which leads to the poor effect of machine learning related to spontaneous signals, especially in related tasks across time, which correspondingly limits the practical use of brain-computer interface (BCI). Objective: In this paper, we proposed a new transfer learning algorithm, which can utilize the labeled motor imagery (MI) EEG data at the previous time to achieve better classification accuracies for a small number of labeled EEG signals at the current time. Methods: We introduced an adaptive layer into the full connection layer of a deep convolution neural network. The objective function of the adaptive layer was designed to minimize the Local Maximum Mean Discrepancy (LMMD) and the prediction error while minimizing the d istance w ithin each c lass (DWC) and maximizing the d istance b etween c lasses w ithin each d omain (DBCWD). We verified the effectiveness of the proposed algorithm on two public datasets. Results: The classification accuracy of the proposed algorithm was higher than other comparison algorithms, and the paired t-test results also showed that the performance of the proposed algorithm was significantly different from that of other algorithms. The results of the confusion matrix and feature visualization showed the effectiveness of the proposed algorithm. Conclusion: Experimental results showed that the proposed algorithm can achieve higher classification accuracy than other algorithms when there was only a small amount of labeled MI EEG data at the current time. It can be promising to be applied to the field of BCI.