Classification of vision perception using eeg signals for brain computer interface / \c Eric Tiong Kung Woo. - Tiong, Eric Kung Woo (School of Mechatronic Engineering, Universiti Malaysia Perlis, 2016)
This research is confined to the design of a suitable BCI system to be applied on an intelligent wheelchair using the visual perception of an individual. Hence discussions in this thesis will be focused on the prediction of the visual perception of a person. Therefore, signal processing procedures, including noise suppression, feature extraction and classification are included to associate the EEG signal emanated by the brain as a post-synaptic response to specific visual stimuli ; five images were used as visual stimuli where the BCI system should be able to classify the five different stimulus responses of EEG signals as five different location. The BCI in this research was designed with the purpose of aiding and supporting the disabled to maneuver around in a controlled environment (i.e. inside a building or a house) in their wheelchair. Hence, the designed semi-autonomous system should be able to support its user to travel freely by performing point-to-point navigational movement ; hence, by limiting the navigational environment, the system therefore recognise each destination as coordinates in a space. These specific locations can then be easily represented as pictures (the five visual stimuli) to be displayed to the user for control purposes. Moreover, the research will be conducted by recording EEG signals from 10 healthy subjects. Visual perception response of the 10 subjects were recorded and used as samples to perform the classification of EEG signals.