Decoding Brain Dynamics in Motor Planning Based on EEG Microstates for Predicting Pedestrian Road-Crossing in Vehicle-to-Everything Architectures (2405.13955v2)

Abstract: Pedestrians who cross roads, often emerge from occlusion or abruptly begin crossing from a standstill, frequently leading to unintended collisions with vehicular traffic that result in accidents and interruptions. Existing studies have predominantly relied on external network sensing and observational data to anticipate pedestrian motion. However, these methods are post hoc, reducing the vehicles' ability to respond in a timely manner. This study addresses these gaps by introducing a novel data stream and analytical framework derived from pedestrians' wearable electroencephalogram (EEG) signals to predict motor planning in road crossings. Experiments were conducted where participants were embodied in a visual avatar as pedestrians and interacted with varying traffic volumes, marked crosswalks, and traffic signals. To understand how human cognitive modules flexibly interplay with hemispheric asymmetries in functional specialization, we analyzed time-frequency representation and functional connectivity using collected EEG signals and constructed a Gaussian Hidden Markov Model to decompose EEG sequences into cognitive microstate transitions based on posterior probabilistic reasoning. Subsequently, datasets were constructed using a sliding window approach, and motor readiness was predicted using the K-nearest Neighbors algorithm combined with Dynamic Time Warping. Results showed that high-beta oscillations in the frontocentral cortex achieved an Area Under the Curve of 0.91 with approximately a 1-second anticipatory lead window before physical road crossing movement occurred. These preliminary results signify a transformative shift towards pedestrians proactively signaling their motor intentions to autonomous vehicles within intelligent V2X systems. The proposed framework is also adaptable to various human-robot interactions, enabling seamless collaboration in dynamic mobile environments.