Motor Imagery Based Fuzzy Logic Controlled Intelligent Wheelchair

This paper shows the feasibility of controlling a wheelchair by Fuzzy Logic control by using the EEG signals. Brain signals used here are extracted by EEG electrodes placed over the motor imagery cortex of the brain. Motor Imagery is the imaginary activity which is responsible for governing the left and the right-hand movements. These signals are utilized for controlling the wheelchair. A Brain Computer Interface (BCI) is used for upholding the interface between the brain and the wheelchair. The EEG signals carry different relevant information is processed for feature extraction and classification and then fed to the Fuzzy Logic controller. Fuzzy interface system (FIS) designed in MATLAB & Simulink is used to achieve the objective. The controller gets the cognitive commands such as forward, backward, left, right and stop signals as inputs. The stop signal prevents any further movement of the wheelchair. The controller outputs are then fed respectively to the motors assigned for different movements of the wheelchair. Some sets of rules are defined between the input and the output to obtain the desired performance. The proposed cost-effective and efficient system using Fuzzy Logic and the obtained surface graphs contribute to the desired performance as expected with intermediate degrees of movements.