Xu Lichao, Xu Minpeng, Ke Yufeng, An Xingwei, Liu Shuang, Ming Dong
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.
Front Hum Neurosci. 2020 Apr 21;14:103. doi: 10.3389/fnhum.2020.00103. eCollection 2020.
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.
跨个体变异性问题阻碍了脑机接口的实际应用。近年来，深度学习因其出色的泛化能力和特征表示能力被引入到脑机接口领域。然而，目前大多数研究仅针对单个数据集验证了深度学习模型，其在其他数据集上的泛化能力仍有待进一步验证。在本文中，我们针对八个运动想象（MI）数据集验证了深度学习模型，并证明跨数据集变异性问题削弱了模型的泛化能力。为减轻跨数据集变异性的影响，我们提出了一种在线预对齐策略，用于在训练和推理过程之前对齐不同个体的脑电图分布。本研究结果表明，采用在线预对齐策略的深度学习模型能够在无需任何额外校准数据的情况下显著提高跨数据集的泛化能力。
