Design of Automated Real-Time BCI Application Using EEG Signals

This study proposed a design of real time BCI application using EEG recording, pre-processing, feature extraction and classification of EEG signals. Recorded EEG signals are highly contaminated by noises and artifacts that originate from outside of cerebral origin. In this study, pre-processing of EEG signals using wavelet multiresolution analysis and independent component analysis is applied to automatically remove the noises and artifacts. Consequently, features of interest are extracted as descriptive properties of the EEG signals. Finally, classification algorithms using artificial neural network is used to distinguish the state of EEG signals for real time BCI application.