Zhao Xueqing, Jin Jing, Xu Ren, Li Shurui, Sun Hao, Wang Xingyu, Cichocki Andrzej
The Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China.
Shenzhen Research Institute of East China University of Technology, Shenzhen, China.
Front Hum Neurosci. 2022 Jun 10;16:875851. doi: 10.3389/fnhum.2022.875851. eCollection 2022.
The P300-based brain-computer interfaces (BCIs) enable participants to communicate by decoding the electroencephalography (EEG) signal. Different regions of the brain correspond to various mental activities. Therefore, removing weak task-relevant and noisy channels through channel selection is necessary when decoding a specific type of activity from EEG. It can improve the recognition accuracy and reduce the training time of the subsequent models. This study proposes a novel block sparse Bayesian-based channel selection method for the P300 speller. In this method, we introduce block sparse Bayesian learning (BSBL) into the channel selection of P300 BCI for the first time and propose a regional smoothing BSBL (RSBSBL) by combining the spatial distribution properties of EEG. The RSBSBL can determine the number of channels adaptively. To ensure practicality, we design an automatic selection iteration strategy model to reduce the time cost caused by the inverse operation of the large-size matrix. We verified the proposed method on two public P300 datasets and on our collected datasets. The experimental results show that the proposed method can remove the inferior channels and work with the classifier to obtain high-classification accuracy. Hence, RSBSBL has tremendous potential for channel selection in P300 tasks.
基于P300的脑机接口（BCI）使参与者能够通过解码脑电图（EEG）信号进行通信。大脑的不同区域对应着各种心理活动。因此，在从脑电图中解码特定类型的活动时，通过通道选择去除与任务相关的弱通道和噪声通道是必要的。它可以提高识别准确率并减少后续模型的训练时间。本研究为P300拼写器提出了一种基于块稀疏贝叶斯的新型通道选择方法。在该方法中，我们首次将块稀疏贝叶斯学习（BSBL）引入到P300脑机接口的通道选择中，并结合脑电图的空间分布特性提出了一种区域平滑BSBL（RSBSBL）。RSBSBL可以自适应地确定通道数量。为确保实用性，我们设计了一种自动选择迭代策略模型，以减少由大尺寸矩阵求逆运算引起的时间成本。我们在两个公开的P300数据集以及我们收集的数据集上验证了所提出的方法。实验结果表明，该方法可以去除劣质通道，并与分类器配合以获得高分类准确率。因此，RSBSBL在P300任务的通道选择方面具有巨大潜力。
