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[2602.18637] Online decoding of rat self-paced locomotion speed from EEG using recurrent neural networks
Summary
This study explores the online decoding of self-paced locomotion speed in rats using non-invasive EEG and recurrent neural networks, achieving high accuracy in decoding speed dynamics.
Why It Matters
Understanding how to decode locomotion speed from EEG non-invasively is crucial for advancements in rehabilitation technologies and brain-computer interfaces. This research provides insights into neural correlates of action and offers a framework for future BCI systems.
Key Takeaways
- The study achieves a high correlation (0.88) in decoding locomotion speed from EEG data.
- Decoding is primarily influenced by visual cortex activity and low-frequency oscillations.
- Pre-training on a single session allows for effective decoding across different sessions for the same rat.
- Cortical states provide information about current, past, and future speed dynamics.
- The findings support the development of non-invasive BCI systems and enhance understanding of neural action representations.
Computer Science > Machine Learning arXiv:2602.18637 (cs) [Submitted on 20 Feb 2026] Title:Online decoding of rat self-paced locomotion speed from EEG using recurrent neural networks Authors:Alejandro de Miguel, Nelson Totah, Uri Maoz View a PDF of the paper titled Online decoding of rat self-paced locomotion speed from EEG using recurrent neural networks, by Alejandro de Miguel and 1 other authors View PDF HTML (experimental) Abstract:$\textit{Objective.}$ Accurate neural decoding of locomotion holds promise for advancing rehabilitation, prosthetic control, and understanding neural correlates of action. Recent studies have demonstrated decoding of locomotion kinematics across species on motorized treadmills. However, efforts to decode locomotion speed in more natural contexts$-$where pace is self-selected rather than externally imposed$-$are scarce, 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. $\textit{Approach.}$ We introduce an asynchronous brain$-$computer interface (BCI) that processes a stream of 32-electrode skull-surface EEG (0.01$-$45 Hz) to decode instantaneous speed from a non-motorized treadmill during self-paced locomotion in head-fixed rats. Using recurrent neural networks and a dataset of over 133 h of recordings, we trained decoders to map ongoing EEG activity to treadmill speed. $\textit{Main results.}...
