Adaptive classification helps hybrid visual brain computer interface systems handle non-stationary cortical signals

The classifier efficiency of the brain-computer interface systems is significantly impacted by the non-stationarity of electroencephalogram (EEG) signals. We propose an adaptive variant of the linear discriminant analysis (LDA) classifier as a solution to this problem. This classifier constantly adjusts its parameters to account for the most recent EEG data. In this study, the authors will update the mean values as well as the covariance matrix of each class pair. Visually evoked cortical potential datasets are used to check how well the proposed classifier performs. The authors prove that the proposed adaptive LDA performs much better than both static multiclass LDA and adaptive PMean LDA.