Knowledge Commons of Institute of Automation,CAS
Calibration-free transfer learning for EEG-based cross-subject motor imagery classification | |
Yihan Wang; Jiaxing Wang; Weiqun Wang![]() ![]() | |
2023-09-28 | |
会议名称 | IEEE Conference on Automation Science and Engineering(IEEE CASE) |
会议日期 | 2023-8-26 |
会议地点 | Auckland, New Zealand |
摘要 | Motor imagery based brain-computer interfaces (MI-BCIs) have been widely used in intelligent medical applications such as post-stroke rehabilitation and mobile assistant robots. However, the high inter-subject variability and the non-stationarity of EEG characteristics limit the cross-subject applications of MI-BCIs. Long-term calibration can be used to improve EEG-based performance, but which will cause low efficiency and reduce practicality. To overcome the limitation, data from other subjects can be used for transfer learning to reduce calibration time. Therefore, a calibration-free transfer learning method for EEG-based cross-subject MI classification is proposed in this paper. On one hand, Euclidean alignment and Riemannian alignment are introduced to reduce domain differences. On the other hand, the similarity is calculated by Multiple Kernel-Maximum Mean Discrepancy (MK-MMD) to select appropriate source domain samples, which is followed by domain adversarial training of neural network (DANN) for the final model construction. In order to achieve calibration-free, the new subjects' resting-state data was used only. Extensive experiments were conducted on BCI competition IV dataset 2a. The results show that the proposed method can achieve 75.96% classification accuracy without using subjects' labeled data, which demonstrates the feasibility of the proposed method in calibration time reduction and classification accuracy improvement. |
七大方向——子方向分类 | 脑机接口 |
国重实验室规划方向分类 | 虚实融合与迁移学习 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57430 |
专题 | 多模态人工智能系统全国重点实验室 |
通讯作者 | Jiaxing Wang |
作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Yihan Wang,Jiaxing Wang,Weiqun Wang,et al. Calibration-free transfer learning for EEG-based cross-subject motor imagery classification[C],2023. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Calibration-Free_Tra(421KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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