通道选择是基于多通道脑电图(EEG)的脑机接口(BCI)的分类程序的关键部分。电极的优化子集降低了计算复杂度并优化了准确性。不同的任务激活大脑中的不同来源,并以独特的通道为特征。当前审查的目标是为四种流行的BCI范例中的每一种定义电极的子集:运动图像,电机执行,稳态视觉诱发电位和P300。已经审查了21项研究,以确定皮质来源的最重要激活。从报告的3DTalairach坐标确定相关的EEG传感器。他们通过加权平均科恩的d和置信区间进行评分,提供相应效应大小的大小及其统计意义。我们的目标是创建一个具有足够统计能力的基于知识的渠道选择框架。核心通道选择(CCS)可以作为脑电图研究人员的参考,具有实用性和快速性等优点,允许半参数算法的简单实现。
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.