期刊
FRONTIERS IN HUMAN NEUROSCIENCE
卷 14, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fnhum.2020.00103
关键词
brain-computer interface; cross-subject variability; cross-dataset variability; deep learning; transfer learning; EEG
资金
- National Key Research and Development Program of China [2017YFB1300302]
- National Natural Science Foundation of China [61976152, 81630051]
- Tianjin Key Technology RD Program [17ZXRGGX00020]
- Young Elite Scientist Sponsorship Program by CAST [2018QNRC001]
Cross-subject variability problems hinder practical usages of Brain-Computer Interfaces. Recently, deep learning has been introduced into the BCI community due to its better generalization and feature representation abilities. However, most studies currently only have validated deep learning models for single datasets, and the generalization ability for other datasets still needs to be further verified. In this paper, we validated deep learning models for eight MI datasets and demonstrated that the cross-dataset variability problem weakened the generalization ability of models. To alleviate the impact of cross-dataset variability, we proposed an online pre-alignment strategy for aligning the EEG distributions of different subjects before training and inference processes. The results of this study show that deep learning models with online pre-alignment strategies could significantly improve the generalization ability across datasets without any additional calibration data.
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