2023/09/21 by Jie Jiao, Jiao, Jie, Meiyan Xu +7
Computer Science · Neuroscience · #68T07 (Primary) #Artificial Intelligence (cs.AI) #Blind Source Separation Techniques #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #FOS: Electrical engineering #Gaze Tracking and Assistive Technology #I.2.4 #Machine Learning (cs.LG) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2309.11714
openalex publication_date 2023/09/21 · openalex created_date 2023/09/23 · openalex updated_date 2026/07/28
There is a correlation between adjacent channels of electroencephalogram (EEG), and how to represent this correlation is an issue that is currently being explored. In addition, due to inter-individual differences in EEG signals, this discrepancy results in new subjects need spend a amount of calibration time for EEG-based motor imagery brain-computer interface. In order to solve the above problems, we propose a Dynamic Domain Adaptation Based Deep Learning Network (DADL-Net). First, the EEG data is mapped to the three-dimensional geometric space and its temporal-spatial features are learned through the 3D convolution module, and then the spatial-channel attention mechanism is used to strengthen the features, and the final convolution module can further learn the spatial-temporal information of the features. Finally, to account for inter-subject and cross-sessions differences, we employ a dynamic domain-adaptive strategy, the distance between features is reduced by introducing a Maximum Mean Discrepancy loss function, and the classification layer is fine-tuned by using part of the target domain data. We verify the performance of the proposed method on BCI competition IV 2a and OpenBMI datasets. Under the intra-subject experiment, the accuracy rates of 70.42% and 73.91% were achieved on the OpenBMI and BCIC IV 2a datasets.