Cursor Movement Control Development by Using ANFIS Algorithm

Our non-invasive brain computer interface uses EEG signals and beta frequency bands over sensorimotor cortex to control cursor movement horizontally (i.e., one-dimension). The main goal of this study is to help people with sever motor disabilities (i.e., Spinal cord injuries) and provide them a new way of communication and control options by which they can move the cursor in one dimension. In this study, offline analysis of the data collected was used to make the user able of controlling the movement of the cursor horizontally (i.e., one dimension). The data was collected during a session in which the user selected among two targets by thinking and moving either the right hand little finger or the left hand little finger. 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 80%. This result suggests that using the adaptive-network based fuzzy inference system algorithm will improve online operation of the current BCI system.