Subject-independent brain computer interface through boosting

This paper presents a subject-independent EEG (Electroencephalogram) classification technique and its application to a P300-based word speller. Due to EEG variations across subjects, a user calibration procedure is usually required to build a subject-specific classification model (SSCM). We remove the user calibration through the boosting of a committee of weak classifiers learned from EEG of a pool of subjects. In particular, we ensemble the weak classifiers based on their confidence that is evaluated according to the classification consistency. Experiments over ten subjects show that the proposed technique greatly outperforms the supervised classification models, hence making P300-based BCIs more convenient for practical uses.