An Optimized Multiclass Support Vector Machine for Recognizing EEG Signals based on a Lower-Limb Exoskeleton

Abstract

The lower-limb prosthesis is used to assist patients with dysfunction of motor dysfunction or aging through Brain-Machine Interface (BMI) based on Electroencephalography (EEG) signals to control cognitive tasks. This paper presents a remarkable model to improve the estimation of the EEG signal and further help improve the control performance for the lower-limb prosthesis, and then improve the rehabilitation. It is based on an optimized Multiclass Support Vector Machine (MSVM) using Snake Optimizer (SO) to get the best possible parameter tuning for classifying different cognitive tasks to control of lower-limb exoskeleton. A public EEG dataset for a lower-limb exoskeleton using Motor Imagery (MI) during the control of the prosthesis and attention to gait (Att) on two surfaces, including flat (Experience) and non-flat (Slopes), has been used as benchmark data sets for this work. The results of the proposed model revealed the superiority of this technique in accuracy, compared with two optimization methods, including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). By comparing the outcomes of SO-MSVM with state of the arts, it achieved an accuracy of more than 85% for MI and Att metric, demonstrating intriguing results for solving the rehabilitation challenge. The devised technique could help people with neurological conditions who have trouble using manual controls.