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

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. But 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 introduce an
asynchronous brain–computer interface (BCI) that processes a stream of cortex-wide,
32-electrode skull-surface EEG recordings (0.01 - 45 Hz) to decode the instantaneous
speed readouts of a non-motorized treadmill during self-paced locomotion in head-fixed
rats. Using recurrent neural networks, our decoding methodology achieves 0.88 correlation
(0.78 R²) 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 for only 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—up to 1000 ms. Our approach may
therefore provide a useful framework for the development of high-performing, non-invasive
BCI applications for locomotion.