EEG-Based Brain-Computer Interface for Actuating Car Commands using the MUSE Headband

This study investigates the development of a Brain-Computer Interface (BCI) for controlling car functions using the MUSE headband, a consumer-grade EEG device. The primary objective is to map EEG signals to key driving commands, including acceleration, braking, and steering, with high accuracy and minimal latency. EEG signals were recorded from 5 participants performing cognitive tasks such as focus, relaxation, and deliberate eye blinks. Preprocessing involved band-pass filtering (1–50 Hz) and artifact removal through Independent Component Analysis (ICA). Features were extracted using Short-Time Fourier Transform (STFT) and classified using Fisher Linear Discriminant Analysis (LDA) and Convolutional Neural Networks (CNN). The results indicate that Fisher LDA achieved an average classification accuracy of 84.6%, while CNNs demonstrated superior performance with an accuracy of 91.3%. Real-time implementation exhibited an average command recognition latency of 320 ms. In simulation tests, the system successfully controlled acceleration, braking, and steering, achieving an overall execution accuracy of 87.8%. These findings demonstrate the feasibility of cost-effective EEG devices for real-time automotive BCI applications, offering significant potential for assistive driving technologies and hands-free vehicle control systems.