Active Data Selection for Motor Imagery EEG Classification
Active Data Selection for Motor Imagery EEG Classification
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DOI：
10.1109/tbme.2014.2358536
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发表时间：
2015-02
影响因子：
4.6
通讯作者：
Naoki Tomida;Toshihisa Tanaka;Shunsuke Ono;M. Yamagishi;Hiroshi Higashi
中科院分区：
文献类型：
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作者：
Naoki Tomida;Toshihisa Tanaka;Shunsuke Ono;M. Yamagishi;Hiroshi Higashi
拒绝或选择来自多个脑电图（EEG）记录试验的数据至关重要。针对脑机接口（brain-machine interfaces，BMIs）问题，提出了一种稀疏感知的方法，用于从运动想象任务中的多个EEG记录中进行数据选择。而不是经验平均的样本协方差矩阵的多个试验，包括低质量的数据，这可能会导致在BMI分类性能差，我们引入加权平均的权重系数，可以拒绝这样的试验。权重系数由导致稀疏权重的最小化问题确定，使得几乎零值被分配给低质量的试验。所提出的方法被成功地应用于估计协方差矩阵的所谓的共同空间模式（CSP）的方法，这是广泛使用的特征提取从EEG中的两类分类。运动想象过程中的脑电信号的分类进行了检查，以支持所提出的方法。应该注意的是，所提出的数据选择方法可以应用于原始CSP方法的许多变型。
Rejecting or selecting data from multiple trials of electroencephalography (EEG) recordings is crucial. We propose a sparsity-aware method to data selection from a set of multiple EEG recordings during motor-imagery tasks, aiming at brain machine interfaces (BMIs). Instead of empirical averaging over sample covariance matrices for multiple trials including low-quality data, which can lead to poor performance in BMI classification, we introduce weighted averaging with weight coefficients that can reject such trials. The weight coefficients are determined by the ℓ1-minimization problem that lead to sparse weights such that almost zero-values are allocated to low-quality trials. The proposed method was successfully applied for estimating covariance matrices for the so-called common spatial pattern (CSP) method, which is widely used for feature extraction from EEG in the two-class classification. Classification of EEG signals during motor imagery was examined to support the proposed method. It should be noted that the proposed data selection method can be applied to a number of variants of the original CSP method.