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Enhancement of mobile development of brain-computer platforms

Advances in Brain-Computer Interfaces (BCIs) have made BCIs come in use mainly for the disabled to communicate. Practical usage of BCIs requires that the whole BCI system be portable so that disabled subjects can use them anywhere. The key aspect in mobility is to use mobile devices for processing by developing software applications with low-computational complexity. In this thesis, a low-computational P300 speller application is developed for Android using an Emotiv wireless EEG neuroheadset. Given the limited resources of mobile devices, a novel ensemble classifier approach is proposed that uses Principal Component Analysis (PCA) features to identify evoked P300 signals from EEG recordings. The performance of the method is demonstrated on benchmark data and on our own data. Results demonstrate the capability of the PCA ensemble classifier to classify P300 data recorded using the Emotiv neuroheadset with an average online classification accuracy of 97.22%.