title: Active data selection for motor imagery EEG classification.
abstract: 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.
authors.forename: Naoki
authors.initials: N
authors.lastname: Tomida
authors.forename: Toshihisa
authors.initials: T
authors.lastname: Tanaka
authors.forename: Shunsuke
authors.initials: S
authors.lastname: Ono
authors.forename: Masao
authors.initials: M
authors.lastname: Yamagishi
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Higashi
mesh_terms: D001931:Brain Mapping / Q000379:methods; D062207:Brain-Computer Interfaces; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology; D007092:Imagination / Q000502:physiology*; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity