EEG-Driven Intention Decoding: Offline Deep Learning Benchmarking on a Robotic Rover

Abstract

Brain–computer interfaces (BCIs) provide a hands-free control modality for mobile robotics, yet decoding user intent during real-world navigation remains challenging. This work presents a brain–robot control framework for offline decoding of driving commands during robotic rover operation. A 4WD Rover Pro platform was remotely operated by 12 participants who navigated a predefined route using a joystick, executing the following commands: forward, reverse, left, right, and stop. Electroencephalogram (EEG) signals were recorded with a 16-channel OpenBCI cap and aligned with motor actions at ∆ = 0 ms and eight future prediction horizons (∆ > 0 ms). After data preprocessing, eleven deep learning (DL) models were benchmarked for the task of intent classification, across the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Transformer architectural families. ShallowConvNet achieved the highest performance for both action prediction (F1-score 67% at ∆ = 0 ms) and intent prediction (F1-score 66% at ∆ = 300 ms), maintaining robust performance at future horizons. By combining real-world robotic control with multi-horizon EEG intention decoding, this study introduces a reproducible benchmark and reveals key design insights for predictive, DL-based BCI systems.