التفاصيل البيبلوغرافية
| العنوان: |
MI-DAGSC: A domain adaptation approach incorporating comprehensive information from MI-EEG signals. |
| المؤلفون: |
Zhang, Dongxue1,2 (AUTHOR) zdx21@mails.jlu.edu.cn, Li, Huiying1,2 (AUTHOR) lihuiying@jlu.edu.cn, Xie, Jingmeng3 (AUTHOR) xjm21@stu.xjtu.edu.cn, Li, Dajun4 (AUTHOR) |
| المصدر: |
Neural Networks. Oct2023, Vol. 167, p183-198. 16p. |
| مصطلحات موضوعية: |
*BRAIN-computer interfaces, *MOTOR imagery (Cognition), *ELECTROENCEPHALOGRAPHY, *BLOCK designs, *NEUROSCIENCES |
| مستخلص: |
Non-stationarity of EEG signals leads to high variability between subjects, making it challenging to directly use data from other subjects (source domain) for the classifier in the current subject (target domain). In this study, we propose MI-DAGSC to address domain adaptation challenges in EEG-based motor imagery (MI) decoding. By combining domain-level information, class-level information, and inter-sample structure information, our model effectively aligns the feature distributions of source and target domains. This work is an extension of our previous domain adaptation work MI-DABAN (Li et al., 2023). Based on MI-DABAN, MI-DAGSC designs Sample-Feature Blocks (SFBs) and Graph Convolution Blocks (GCBs) to focus on intra-sample and inter-sample information. The synergistic integration of SFBs and GCBs enable the model to capture comprehensive information and understand the relationship between samples, thus improving representation learning. Furthermore, we introduce a triplet loss to enhance the alignment and compactness of feature representations. Extensive experiments on real EEG datasets demonstrate the effectiveness of MI-DAGSC, confirming that our method makes a valuable contribution to the MI-EEG decoding. Moreover, it holds great potential for various applications in brain–computer interface systems and neuroscience research. And the code of the proposed architecture in this study is available under https://github.com/zhangdx21/MI-DAGSCTest. • A model for reducing individual variability and leveraging all subjects' labeled data. • A domain adaptive method considering comprehensive information. • A framework for exploring inter-sample knowledge in EEG signals. • Non-shared Adversarial Method for Precise Distribution Alignment. [ABSTRACT FROM AUTHOR] |
| قاعدة البيانات: |
Academic Search Index |
