An EOG based communication channel for paralyzed patients

In this study, Electrooculogram (EOG) based Human-Machine Interface (HMI) application is proposed for partial or completely paralyzed and physically limited people as Amyotrophic Lateral Sclerosis (ALS) patients. In the designed system, EOG signals consisted of vertical and horizontal eye movements are detected by using 6 Ag-AgCl electrodes which placed around the eye. Then, by applying amplifying and filtering processes to the detected signals, EOG signals at 0-+5V amplitude levels are obtained at the output of analog stage. In consequence of digital processing of analog EOG data with microcontroller unit, control signals for HMI applications are acquired. At signal processing stage, after preprocessing step for eye movements (vertical, horizontal and blink) maximum and minimum voltage amplitude values are detected. These values are directly determine the performance of the classification process. K-Nearest Neighbor (k-NN) classifier and Support Vector Machines (SVM) are used for classification. According to the results, k-NN and SVM perform the classification task with %90.3 and %92.6 accuracy, respectively. Simulation results show that with the proposed EOG based HMI system, physically limited patients can communicate with their environments in a successful manner.