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

TL;DR

This study tackles the challenge of decoding self-paced locomotion speed from non-invasive brain signals. Using cortex-wide skull EEG in head-fixed rats and end-to-end LSTM-based decoding, the authors achieve a median correlation of $r$ = $0.88$ and $R^2$ = $0.78$, significantly outperforming linear baselines. They reveal that visual cortex activity and low-frequency bands ($<8$ Hz) are primary drivers, and that neural signatures generalize across sessions within the same subject but require transfer learning for cross-subject generalization. The work demonstrates the feasibility of accurate, continuous speed decoding with non-invasive EEG and highlights practical implications for real-time BCIs and our understanding of distributed neural representations of action dynamics.

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. $\textit{Main results.}$ Our decoding achieves a correlation of 0.88 ($R^2$ = 0.78) for speed, primarily driven by visual cortex electrodes and low-frequency ($< 8$ Hz) oscillations. Moreover, pre-training on a single session permitted decoding on other sessions from the same rat, suggesting uniform neural signatures that generalize across sessions but fail to transfer across animals. Finally, we found that cortical states not only carry information about current speed, but also about future and past dynamics, extending up to 1000 ms. $\textit{Significance.}$ These findings demonstrate that self-paced locomotion speed can be decoded accurately and continuously from non-invasive, cortex-wide EEG. Our approach provides a framework for developing high-performing, non-invasive BCI systems and contributes to understanding distributed neural representations of action dynamics.

Figures (7)

• Figure 1: Conceptual diagram of the decoding strategy. This schematic is provided for visualization purposes only.
• Figure 2: Schematic of training strategies and data splits for assessing generalization of locomotion-related neural signatures across sessions and animals.Models were evaluated under three strategies: single-session training, where decoders were trained from scratch on either 80% or 10% of the target session; zero-shot generalization, where a model trained on a different session with the same animal (cross-session) or a different subject (cross-subject) was directly applied to the target session without further training; and transfer learning, where pre-trained cross-session or cross-subject models were fine-tuned using only 10% of the target session. Each bar represents a data partition: training (TR), validation (V), and test (TS), with numbers indicating the proportion of session data used.
• Figure 3: Recurrent neural networks with LSTM units achieve high-precision decoding of treadmill speed on a sample-by-sample basis.A) Decoding performance distribution across sessions for different machine- and deep-learning models. Each histogram represents the performance of individual sessions, measured by correlation (r, left) and coefficient of determination (R², right). The red horizontal lines indicate the median performance across sessions. B) Scatter plot of actual vs. decoded treadmill speeds (at 10ms intervals) on test data for one of the top 5% best-performing sessions. Speeds are decoded using the RNN, which achieved the highest overall performance across sessions. C) Example speed trace from a 20-second segment of the session shown in panel B, comparing actual (in black) and decoded (in red) treadmill speeds over time.
• Figure 4: Neural signatures of locomotion are conserved across sessions on multiple days within a single subject. Decoding performance distribution for different RNN models across three training strategies: single-session from scratch (80% training in dark green, 10% training in pink); zero-shot—i.e., using a model trained on another session of the same animal (cross session, in light green) or from another animal (cross animal, in yellow) and fine-tuning (i.e., transfer learning) a pre-trained model (cross-session in orange, cross-subject in blue). Performance is shown for correlation (r, left) and coefficient of determination (R², right). The numbers inside the boxes show the median value of that metric. The top section displays boxplots without outliers, while the bottom section shows violin plots of the outliers separately to improve the visualization of the overall data distribution. (The cross-subject zero-shot strategy, in yellow, did not exhibit outliers in the correlation measurements.)
• Figure 5: Visual cortex is the predominant contributor to locomotion decoding. Decoding performance across sessions for RNN models trained and tested on five specific brain regions (diagonal elements) and on all pairs of regions (off-diagonal elements); measured by the median correlation (r, left) and coefficient of determination (R², right).