An adaptive accuracy-weighted ensemble for inter-subjects classification in brain-computer interfacing

Learning from other subjects and/or sessions led to considerable reduction of calibration time in EEG-based BCIs. However, such learning scheme is not straightforward because of the non-stationary nature of EEG signals. In this paper, we propose an adaptive accuracy-weighted ensemble (AAWE) approach that allows tracking non-stationarity in EEG signals and effectively learning from other subjects. It consists of an ensemble of classifiers, each of which is trained using data recorded from one BCI user. Classifiers' weights are initialized according to their accuracy in classifying calibration data of current BCI user. These weights are updated using ensemble decision during feedback phase, when there is no information about true class labels. The effectiveness of our approach is demonstrated through an empirical comparison with other state of the art classifiers combination strategies.