Cross-Dataset Variability Problem in EEG Decoding With Deep Learning
Cross-Dataset Variability Problem in EEG Decoding With Deep Learning
复制标题
DOI：
10.3389/fnhum.2020.00103
复制
发表时间：
2020-04
影响因子：
2.9
通讯作者：
Lichao Xu;Minpeng Xu;Yufeng Ke;X. An;Shuang Liu;Dong Ming
中科院分区：
文献类型：
--
作者：
Lichao Xu;Minpeng Xu;Yufeng Ke;X. An;Shuang Liu;Dong Ming
跨学科可变性问题阻碍了脑机接口的实际应用。最近，深度学习因其更好的泛化和特征表示能力被引入脑机接口社区。然而，目前大多数研究仅对单个数据集的深度学习模型进行了验证，对其他数据集的泛化能力还有待进一步验证。在本文中，我们对8个MI数据集的深度学习模型进行了验证，并证明了跨数据集变异性问题削弱了模型的泛化能力。为了减轻跨数据集可变性的影响，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受试者的脑电分布。本研究结果表明，采用在线预对齐策略的深度学习模型可以显著提高跨数据集的泛化能力，而无需额外的校准数据。
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.