Mobile Robot based BCI Uses EEG-P300 Signals Classified by ANFIS Method


Our non-invasive brain computer interface uses EEG-P300 signals over visual cortex to control electronic wheelchair movement (i.e., forward, backward, left, and right). In this study, offline analysis of the data collected was used to make the user able of controlling the movement of the mobile robot. The data was collected during a session in which four subjects with age about 242 years were tested. The adaptive-network based fuzzy inference system algorithm was examined for the classification method with some parameters. In the offline analysis, the method used showed a significant performance in the classification accuracy level and it gave an accuracy level of more than 92 persen. This result suggests that using the adaptive-network based fuzzy inference system algorithm will improve real time operation of the current BCI system.

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