State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, People's Republic of China.
Behav Res Methods. 2023 Jun;55(4):1980-2003. doi: 10.3758/s13428-022-01897-2. Epub 2022 Jul 6.
Channel selection is a critical part of the classification procedure for multichannel electroencephalogram (EEG)-based brain-computer interfaces (BCI). An optimized subset of electrodes reduces computational complexity and optimizes accuracy. Different tasks activate different sources in the brain and are characterized by distinctive channels. The goal of the current review is to define a subset of electrodes for each of four popular BCI paradigms: motor imagery, motor execution, steady-state visual evoked potentials and P300. Twenty-one studies have been reviewed to identify the most significant activations of cortical sources. The relevant EEG sensors are determined from the reported 3D Talairach coordinates. They are scored by their weighted mean Cohen's d and its confidence interval, providing the magnitude of the corresponding effect size and its statistical significance. Our goal is to create a knowledge-based channel selection framework with a sufficient statistical power. The core channel selection (CCS) could be used as a reference by EEG researchers and would have the advantages of practicality and rapidity, allowing for an easy implementation of semiparametric algorithms.
通道选择是多通道脑电（EEG）脑机接口（BCI）分类过程的关键部分。电极的优化子集可以降低计算复杂度并优化准确性。不同的任务激活大脑中的不同源，并具有独特的通道特征。本综述的目的是为四种流行的 BCI 范式中的每一种定义一个电极子集：运动想象、运动执行、稳态视觉诱发电位和 P300。为了确定皮质源的最显著激活，我们回顾了 21 项研究。相关的 EEG 传感器是从报告的 3D Talairach 坐标中确定的。根据加权平均 Cohen's d 及其置信区间对其进行评分，提供相应的效应量大小及其统计显著性。我们的目标是创建一个具有足够统计能力的基于知识的通道选择框架。核心通道选择（CCS）可作为 EEG 研究人员的参考，具有实用性和快速性的优点，允许轻松实现半参数算法。
