As mobile robots increasingly operate in environments shared with humans, proactively anticipating human motion rather than responding reactively is critical for preempting collisions during close-proximity navigation, while maintaining mobility efficiency and avoiding unnecessary yields. A timely and motivating engineering application is how autonomous vehicles interpret ambiguous right-of-way such as unsignalized pedestrian crossings. To address this challenge, this study explores the feasibility of decoding preparatory neural activity from wearable electroencephalography (EEG) to predict human motion intention before it is behaviorally expressed. Drawing inspiration from biological predictive coding mechanisms between the sensorimotor cortex and insula-frontoparietal network, we implement this principle in a Temporal Convolutional Network-Transformer architecture to decode fast-evolving EEG signals underlying perception-action transitions. In experiments involving twelve participants simulating road-crossing decisions under varying traffic volume, marked crosswalks, and traffic signals, neurophysiological analyses reveal hemispheric asymmetries in functional specialization and identify high-beta oscillations (16-25 Hz) in the right fronto-central region (F4) as robust neural markers of motor readiness and decision commitment. Using sliding-window feature extraction, we benchmarked sixteen classification models across traditional, recurrent, and convolutional deep learning architectures, and found that our approach achieved the highest Area Under the Curve (AUC) of 0.727 with an approximate 1-second look-ahead. This work demonstrates how biologically grounded temporal architectures can enhance anticipatory intelligence in autonomous systems and represents the first step toward proactive and adaptive human-robot interaction in the built environment.