EEG-Based Brain-Computer Interface (BCI) Controlled Robotic Arm

Individuals with Amyotrophic Lateral Sclerosis (ALS) or spinal cord injury struggle while performing everyday tasks, which imposes a great challenge on their life quality. Recognizing the limitations of existing prosthetics, we present a novel wireless Brain-Computer Interface (BCI)-controlled robotic arm system. BCI is a method of communicating the human brain with an external device; It uses neural activity as a control signal to take actions, actuate and communicate with the world directly using brain integration with peripheral devices and systems, such as a robotic limb, computer cursor, or functional electrical stimulation device. This project targets ALS patients specifically and patients with severe motor impairments generally. EEG blinking signals were selected as a non-invasive control signal, overcoming the prevailing issues of unreliability and high costs associated with prosthetics in Egypt. The research objectives encompass the design and implementation of a BCI system capable of capturing EEG blinking signals from the prefrontal cortex to precisely control the humanoid robotic arm and perform essential actions. This includes programming microcontrollers for versatile hand movements, integrating the BCI-controlled robotic arm system, and evaluating its real-time performance on different test subjects. Results showcased the successful integration of the NeuroSky MindWave headset for controlling the robotic arm with $77.5 \%$ accuracy in executing the desired motion. This paper unravels the detailed process of fabricating the BCIcontrolled robotic arm system. These technologies in real-time scenarios, from capturing brain waves to 3D printing of a humanoid robotic arm, make us think about the transformative potential of BCI in improving accessibility and independence for people with motor impairments. BCI placed the cornerstone of assistive technology research leading to state-of-the-art, accurate, economical, and versatile applications.