This paper presents a novel approach of spectral feature selection using spatial filters for the classification of four cognitive imagery tasks. The input dataset consists of electroencephalogram (EEG) signals acquired through a commercial wireless headset. The spectral features included mel frequency (MF) components extracted from the low frequency bands of EEG signal. A spatial projection filter was used for the selection of the most relevant features before classification. The popular method of multiclass common spatial pattern (CSP) and regularized CSP (RCSP) are investigated for a subject dependent (intra) and subject independent (inter) generation of spatial projection filter, respectively. Based upon this, present study used two different algorithmic approaches namely MF-CSP and MF-RCSP. The developed algorithm successfully classified four imagery actions with the reported prediction accuracy of 46.23% and 64.01% and standard deviation of 11.60% and 8.67% for MF-CSP and MF-RCSP, respectively.

中文翻译：

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使用基于 CSP 的特征选择方法对 EEG 信号进行认知图像分类

本文提出了一种使用空间滤波器对四种认知图像任务进行分类的光谱特征选择的新方法。输入数据集由通过商用无线耳机获取的脑电图 (EEG) 信号组成。频谱特征包括从 EEG 信号的低频带中提取的梅尔频率 (MF) 分量。空间投影过滤器用于在分类之前选择最相关的特征。分别针对空间投影滤波器的主题相关（内部）和主题独立（间）生成研究了多类公共空间模式（CSP）和正则化 CSP（RCSP）的流行方法。基于此，本研究使用了两种不同的算法方法，即 MF-CSP 和 MF-RCSP。