Tang Chao, Gao Tianyi, Li Yuanhao, Chen Badong
Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China.
Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan.
Front Neurosci. 2022 Oct 21;16:1045851. doi: 10.3389/fnins.2022.1045851. eCollection 2022.
Brain-computer interfaces (BCIs) based on motor imagery (MI) utilizing multi-channel electroencephalogram (EEG) data are commonly used to improve motor function of people with motor disabilities. EEG channel selection can enhance MI classification accuracy by selecting informative channels, accordingly reducing redundant information. The sequential backward floating search (SBFS) approach has been considered as one of the best feature selection methods. In this paper, SBFS is first implemented to select the optimal EEG channels in MI-BCI. Further, to reduce the time complexity of SBFS, the modified SBFS is proposed and applied to left and right hand MI tasks. In the modified SBFS, based on the map of EEG channels at the scalp, the symmetrical channels are selected as channel pairs and acceleration is thus realized by removing or adding multiple channels in each iteration. Extensive experiments were conducted on four public BCI datasets. Experimental results show that the SBFS achieves significantly higher classification accuracy ( < 0.001) than using all channels and conventional MI channels (i.e., C3, C4, and Cz). Moreover, the proposed method outperforms the state-of-the-art selection methods.
基于运动想象（MI）并利用多通道脑电图（EEG）数据的脑机接口（BCI）通常用于改善运动功能障碍患者的运动功能。脑电图通道选择可以通过选择信息丰富的通道来提高运动想象分类准确率，从而减少冗余信息。顺序反向浮动搜索（SBFS）方法被认为是最佳特征选择方法之一。本文首先实现了SBFS以在运动想象脑机接口中选择最优脑电图通道。此外，为了降低SBFS的时间复杂度，提出了改进的SBFS并将其应用于左右手运动想象任务。在改进的SBFS中，基于头皮上的脑电图通道图，选择对称通道作为通道对，从而通过在每次迭代中移除或添加多个通道来实现加速。在四个公开的脑机接口数据集上进行了大量实验。实验结果表明，与使用所有通道和传统运动想象通道（即C3、C4和Cz）相比，SBFS实现了显著更高的分类准确率（<0.001）。此外，所提出的方法优于当前最先进的选择方法。
