脑-机接口
脑电图
计算机科学
选择(遗传算法)
运动表象
频道(广播)
人工智能
接口(物质)
模式识别(心理学)
机器学习
神经科学
心理学
计算机网络
最大气泡压力法
气泡
并行计算
作者
E. Guttmann-Flury,Xinjun Sheng,Xiangyang Zhu
标识
DOI:10.3758/s13428-022-01897-2
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI