EEG-Based Brain-Controlled Wheelchair with Four Different Stimuli Frequencies

The paper considers a non-invasive brain computer interface uses EEG-SSVEP signals over visual cortex to control electronic wheelchair movement (i.e., forward, backward, left, and right). The main goal of the paper is to help people with severe motor disabilities (i.e., spinal cord injuries) and to provide them with a new way of communication and control options. In this paper, offline analysis of the data collected is used to make the user able of controlling the movement of the electronic wheelchair. The data are collected during a session in which four subjects with age about 25±1 years were tested. The adaptivenetwork based fuzzy inference system algorithm is then applied for the classification method with some parameters. In the offline analysis, the implemented method shows a significant performance in the classification accuracy level and it gives an accuracy level of more than 90%. This result suggests that using the adaptive-network based fuzzy inference system algorithm can improve real time operation of the current BCI system.