A principal component analysis ensemble classifier for P300 speller applications

Recent advances in developing Brain-Computer Interfaces (BCIs) have opened up a new realm for designing efficient systems that could enable disabled people to communicate. The P300 speller is one important BCI application that allows the selection of characters on a virtual keyboard by analyzing recorded electroencephalography (EEG) activity. In this work, we propose an ensemble classifier that uses Principal Component Analysis (PCA) features to identify evoked P300 signals from EEG recordings. We examine the performance of the proposed method, using different linear classifiers, on the datasets provided by the BCI competition III. Results demonstrate a classification accuracy of 91% using the proposed method. In addition, our results indicate a significant improvement in classification accuracy compared to traditional feature extraction and classification approaches. The proposed method results in low across-subjects variability compared to other methods with minimal parameter tuning required which could be useful in mobile platform P300 applications.