An Automatic Approach to Control Wheelchair Movement for Rehabilitation Using Electroencephalogram

Abstract

Modern science and technology development enables humans to control devices using their brains. brain computer interface (BCI) system using EEG (Electroencephalogram) is a non-invasive method that uses brain signals to control robots. Brain control robots can assist people with disabilities to improve their livelihood. In our proposed work to control wheelchair movement for rehabilitation using EEG, electrodes are first placed on the subject's scalp to acquire brain signal activity. After that, the signals got filtered using fast fourier transformation (FFT) method, and then features got extracted using power spectral density (PSD). Random forest is used for the classification of wheelchair movement. For this purpose, a publicly available dataset from Kaggle is used, and an average accuracy of 96.79 is achieved. The proposed architecture has outperformed all the existing ones in its concerned domain; thus, it is suitable, cost-effective, and flexible for the users, which also helps maintain user privacy.