Neural Command: Real-Time EEG-Based Brain-Computer Interface for Assistive Robotic Control

This paper presents a method to help people with disabilities interact with their surroundings using brainwave signals. Since individuals with disabilities often depend on different types of transportation, this research focuses on enabling the real-time control of a robotic vehicle through non-invasive brain-computer interfaces (BCI).Can brain-computer interfaces (BCIs) based on EEG signals control a robotic car in real time? The MUSE headband is used to capture brain waves, and the signals are processed through PETAL metrics and a server within a Brain Wave Processing Unit. Subsequently, they are transmitted via WiFi/IPv4 to a Vehicle Control Unit with esp8266 microcontroller. The system underwent rigorous evaluation, achieving an 85% control accuracy and an average response time of 300 milliseconds. These results highlight the system’s effectiveness in real-time applications, offering precise and rapid response capabilities suitable for assistive technology. This research represents a contribution to the development of advanced BCI-based assistive devices, demonstrating the potential for improved human-machine interaction. The high accuracy and low latency of the system emphasize its suitability for environments where swift and reliable control is crucial, particularly for individuals with mobility impairments.