Learning adaptive subject-independent P300 models for EEG-based brain-computer interfaces

This paper proposes an approach to learn subject-independent P300 models for EEG-based brain-computer interfaces. The P300 models are first learned using a pool of existing subjects and Fisher linear discriminant, and then autonomously adapted to the unlabeled data of a new subject using an unsupervised machine learning technique. In data analysis, we apply this technique to a set of EEG data of 10 subjects performing word spelling in an oddball paradigm. The results are very positive: the adapted models with unlabeled data yield virtually the same classification accuracy as the conventional methods with labeled data. Therefore, it proves the feasibility of P300-based BCIs which can be applied directly to a new subject without training sessions.