School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel). 2022 Aug 31;22(17):6572. doi: 10.3390/s22176572.
A brain-computer interface (BCI) translates a user's thoughts such as motor imagery (MI) into the control of external devices. However, some people, who are defined as BCI illiteracy, cannot control BCI effectively. The main characteristics of BCI illiterate subjects are low classification rates and poor repeatability. To address the problem of MI-BCI illiteracy, we propose a distribution adaptation method based on multi-kernel learning to make the distribution of features between the source domain and target domain become even closer to each other, while the divisibility of categories is maximized. Inspired by the kernel trick, we adopted a multiple-kernel-based extreme learning machine to train the labeled source-domain data to find a new high-dimensional subspace that maximizes data divisibility, and then use multiple-kernel-based maximum mean discrepancy to conduct distribution adaptation to eliminate the difference in feature distribution between domains in the new subspace. In light of the high dimension of features of MI-BCI illiteracy, random forest, which can effectively handle high-dimensional features without additional cross-validation, was employed as a classifier. The proposed method was validated on an open dataset. The experimental results show that that the method we proposed suits MI-BCI illiteracy and can reduce the inter-domain differences, resulting in a reduction in the performance degradation of both cross-subjects and cross-sessions.
脑机接口（BCI）将用户的思维，如运动想象（MI），转化为对外部设备的控制。然而，有些人被定义为 BCI 文盲，无法有效地控制 BCI。BCI 文盲受试者的主要特征是分类率低和可重复性差。为了解决 MI-BCI 文盲问题，我们提出了一种基于多核学习的分布适应方法，使源域和目标域之间的特征分布更加接近，同时最大化类别可分性。受核技巧的启发，我们采用了基于多核的极限学习机对标记的源域数据进行训练，以找到一个新的高维子空间，该子空间最大化数据可分性，然后使用基于多核的最大均值差异进行分布适应，以消除新子空间中域间特征分布的差异。针对 MI-BCI 文盲的高维特征，我们采用随机森林作为分类器，该方法可以有效地处理高维特征，而无需额外的交叉验证。所提出的方法在一个公开数据集上进行了验证。实验结果表明，我们提出的方法适用于 MI-BCI 文盲，可以减少域间差异，从而降低跨受试者和跨会话的性能下降。
