Low-Dimensional Subject Representation-Based Transfer Learning in EEG Decoding
Low-Dimensional Subject Representation-Based Transfer Learning in EEG Decoding
复制标题
脑电解码中基于低维主体表征的迁移学习
DOI：
10.1109/jbhi.2020.3025865
复制
发表时间：
2021-06-01
影响因子：
7.7
通讯作者：
Wang, Li-Chun
中科院分区：
文献类型：
--
作者：
Jeng, Po-Yuan;Wei, Chun-Shu;Wang, Li-Chun
近年来，基于脑电(EEG)的无源脑机接口(BCI)的发展为现实世界的神经监测技术提供了新的思路。然而，人类脑电活动的多变性阻碍了基于脑电信号的脑机接口的实际应用。为了解决这个问题，许多迁移学习技术执行有监督的校准。这种校准方法需要与任务相关的数据，这在现实生活场景中是不切实际的，例如驾驶时昏昏欲睡。本研究提出了一种基于实验前脑电信号低维表征的脑电信号解码的迁移学习框架。将张量分解应用于受试者的试前脑电信号，以提取被试在被试、空间域和谱域的潜在特征。然后，该框架对特征进行评估，以获得低维的主题表征，从而能够识别具有相似脑动力学的主题。这种方法可以利用来自其他用户的现有数据，以及来自新用户的快速、非任务、无监督校准的少量数据来构建准确的BCI。我们的结果表明，在预测精度方面，基于低维主题表征的迁移学习(LDSR-TL)框架在认知状态跟踪方面优于随机选择和Riemannian流形方法，并且需要更少的训练数据。研究结果可以大大提高基于脑电信号的脑-机接口在现实世界中的实用性和可用性。
Recently, the advances in passive brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have shed light on real-world neuromonitoring technologies. However, human variability in the EEG activities hinders the development of practical applications of EEG-based BCI. To tackle this problem, many transfer-learning techniques perform supervised calibration. This kind of calibration approach requires task-relevant data, which is impractical in real-life scenarios such as drowsiness during driving. This study presents a transfer-learning framework for EEG decoding based on the low-dimensional representations of subjects learned from the pre-trial EEG. Tensor decomposition was applied to the pre-trial EEG of subjects to extract the underlying characteristics in subject, spatial, and spectral domains. Then, the proposed framework assessed the characteristics to obtain the low-dimensional subject representations such that the subjects with similar brain dynamics can be identified. This method can leverage the existing data from other users, and a small number of data from a rapid, non-task, unsupervised calibration from a new user to build an accurate BCI. Our results demonstrated that, in terms of prediction accuracy, the proposed low-dimensional subject representation-based transfer learning (LDSR-TL) framework outperformed the random selection, and the Riemannian manifold approach in cognitive-state tracking, while requiring fewer training data. The results can greatly improve the practicability, and usability of EEG-based BCI in the real world.