Online decoding of rat self-paced locomotion speed from EEG using recurrent neural networks.

OBJECTIVE: Accurate and reliable neural decoding of locomotion holds promise for advancing clinical applications such as rehabilitation and prosthetic control, as well as for understanding neural correlates of action. Recent studies have demonstrated successful decoding of locomotion kinematics across species in motorized treadmill settings. However, efforts to decode locomotion speed directly and continuously in more natural contexts-where pace is self-selected rather than externally imposed-are scarce, and those that exist generally achieve only modest accuracy and require intracranial implants. Here, we aim to decode self-paced locomotion speed non-invasively and continuously using cortex-wide EEG recordings from rats. APPROACH: We introduce an asynchronous brain-computer interface (BCI) that processes a continuous stream of 32-electrode skull-surface EEG recordings (0.01-45 Hz) to decode instantaneous speed readouts from a non-motorized treadmill during self-paced locomotion in head-fixed rats. Using recurrent neural networks and a large dataset comprising over 133 h of recordings, we trained decoders to map ongoing EEG activity to treadmill speed. MAIN RESULTS: Our decoding methodology achieves a correlation of 0.88 (R² = 0.78) for speed, primarily driven by visual cortex electrodes and low-frequency (< 8 Hz) oscillations. Moreover, pre-training on a single recording session permitted decoding on other sessions from the same rat, suggesting the presence of uniform neural signatures of locomotion that generalize across sessions but fail to transfer across animals. Finally, we found that cortical states not only carry precise information about the current speed, but also about future and past dynamics, extending up to 1000 ms. SIGNIFICANCE: These findings demonstrate that self-paced locomotion speed can be decoded accurately and continuously from non-invasive, cortex-wide EEG. Our approach may provide a useful framework for developing high-performing, non-invasive BCI systems for locomotion and contribute to understanding distributed neural representations of action dynamics.