Empowering the Disabled with Brain-Computer Interface

This work presents a Brain-Computer Interface (BCI) system designed to help persons suffering from severe motor function loss due to accidents or diseases. The system enables users to control and interact with their external environment through neural signals. An Ag/AgCl electrode cap is placed on the scalp to capture the brain’s electrical activity, while an RMS 32-channel Electroencephalogram (EEG) data acquisition system amplifies the signals. The recorded signals from multiple subjects are converted into Excel format using suitable software. These data files are then processed in MATLAB using a similarity matching algorithm to detect patterns in event-related potentials. Based on the processed brain signals, the system performs specific actions, such as switching a fan or light bulb on or off through relays. Control of these external devices is achieved using an Arduino microcontroller. The system shows promising accuracy of 95% in translating brain activity into actionable commands and controlling external devices, offering potential for improved independence and quality of life for affected individuals. The proposed BCI system offers a potential communication pathway for persons with limited mobility, assisting them to interact with their surroundings more effectively.