Shin Jaeyoung
Department of Electronic Engineering, Wonkwang University, Iksan, South Korea.
Front Hum Neurosci. 2020 Jul 17;14:236. doi: 10.3389/fnhum.2020.00236. eCollection 2020.
The feasibility of the random subspace ensemble learning method was explored to improve the performance of functional near-infrared spectroscopy-based brain-computer interfaces (fNIRS-BCIs). Feature vectors have been constructed using the temporal characteristics of concentration changes in fNIRS chromophores such as mean, slope, and variance to implement fNIRS-BCIs systems. The mean and slope, which are the most popular features in fNIRS-BCIs, were adopted. Linear support vector machine and linear discriminant analysis were employed, respectively, as a single strong learner and multiple weak learners. All features in every channel and available time window were employed to train the strong learner, and the feature subsets were selected at random to train multiple weak learners. It was determined that random subspace ensemble learning is beneficial to enhance the performance of fNIRS-BCIs.
为提高基于功能近红外光谱的脑机接口（fNIRS-BCIs）的性能，探讨了随机子空间集成学习方法的可行性。利用功能近红外光谱发色团浓度变化的时间特征（如均值、斜率和方差）构建特征向量，以实现fNIRS-BCIs系统。采用了fNIRS-BCIs中最常用的均值和斜率特征。分别使用线性支持向量机和线性判别分析作为单个强学习器和多个弱学习器。利用每个通道和可用时间窗口中的所有特征来训练强学习器，并随机选择特征子集来训练多个弱学习器。结果表明，随机子空间集成学习有利于提高fNIRS-BCIs的性能。
