title: Performance analysis of a Principal Component Analysis ensemble classifier for Emotiv headset P300 spellers.
abstract: The current trend to use Brain-Computer Interfaces (BCIs) with mobile devices mandates the development of efficient EEG data processing methods. In this paper, we demonstrate the performance of a Principal Component Analysis (PCA) ensemble classifier for P300-based spellers. We recorded EEG data from multiple subjects using the Emotiv neuroheadset in the context of a classical oddball P300 speller paradigm. We compare the performance of the proposed ensemble classifier to the performance of traditional feature extraction and classifier methods. Our results demonstrate the capability of the PCA ensemble classifier to classify P300 data recorded using the Emotiv neuroheadset with an average accuracy of 86.29% on cross-validation data. In addition, offline testing of the recorded data reveals an average classification accuracy of 73.3% that is significantly higher than that achieved using traditional methods. Finally, we demonstrate the effect of the parameters of the P300 speller paradigm on the performance of the method.
authors.forename: Amr S
authors.initials: AS
authors.lastname: Elsawy
authors.forename: Seif
authors.initials: S
authors.lastname: Eldawlatly
authors.forename: Mohamed
authors.initials: M
authors.lastname: Taher
authors.forename: Gamal M
authors.initials: GM
authors.lastname: Aly
mesh_terms: D000465:Algorithms; D062207:Brain-Computer Interfaces; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods; D018913:Event-Related Potentials, P300; D006801:Humans; D007802:Language; D008297:Male; D025341:Principal Component Analysis