EEG—Brainwaves Signal Based BCI Control Wheel Chair System

Abstract

AbstractThe movement with man-less support for a physically disabled or challenged people is a challenging task. Many techniques had been established such as joystick-based wheel chair movement, eyeball movement-based wheel chair movement control. But, these methods were executed and resulted with drawbacks and constraints. This made to enhance the working model by utilizing the brain signals for controlling the wheel chair. The most significant part of the human body to control wholesome is the brain whose cognitive behavior makes the researchers to perform various tests on it for identifying and studying the functionalities. Though various methods are available for studying the brain functionality, study through the electroencephalography (EEG) signals was preferred in designing the working model such as the wheel chair system with man-less control drive for the physically challenged people or patients. The human brain controls the entire body through the interconnected neurons by generating waves or signals which support in controlling the wheel chair. In the proposed work, EEG signals are considered for activating the wheel chair movement. The wave patterns differ based on the patient thoughts and emotions in turn the generated electrical signals. The proposed work has been carried over by pre-processing where Notch filter is used, followed by feature extraction where FFT is used. The extracted signals are then used for training the machine language (ML) model. The classified signals are compared, and an optimistic approach has been identified to derive the physical model. The metrics evaluated for comparison are accuracy, sensitivity, precision and specificity. The optimistic signals are utilized for controlling the 360\(^{\circ }\) movement of the wheel chair. The acquired EEG signals are communicated through wireless to the model after converting the waves into mental commands through appropriate software for a human-less support.KeywordsMLEEGBrainwaves signalBCI