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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(More)
Common Spatial Pattern (CSP) methods are widely used to extract the brain activity for brain machine interfacing (BMI) based on electroencephalogram (EEG). For each mental task, CSP methods estimate a covariance matrix of EEG signals and adopt the uniform average of the sample covariance matrices over trials. However, the uniform average is sensitive to(More)
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