Data Selection for Motor Imagery EEG Classification
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 EEGsignals 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.
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