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EEG alphabet speller with Neural Network classifier for P300 signal detection

This paper presents the implementation of a P300 alphabet speller study case. The speller is based on methodology in which alphabet symbols are displayed in real-time on screen to test subjects, with real-time electroencephalograph measurement of human brainwaves, with P300 event evoked potential signal detection. The implementation is done in MATLAB with Emotiv Epoc EEG+ headset.