TRACE: Temporal Routing with Autoregressive Cross-channel Experts for EEG Representation Learning

2026-05-11

AI Summary

This work addresses the challenge of learning transferable representations from electroencephalography (EEG) signals, which exhibit complex multi-channel coupling and non-stationarity. To this end, the authors propose TRACE, a framework that leverages autoregressive prediction of future EEG segments and introduces a causal cross-channel dynamic expert routing mechanism at each time step. This design enables time-adaptive activation of heterogeneous computational pathways while preserving instantaneous channel consistency. By integrating autoregressive pretraining, causal context modeling, and cross-channel collaborative computation, TRACE supports unsupervised pretraining on unlabeled, heterogeneous EEG data. Evaluated across eight downstream EEG benchmark tasks, the method achieves state-of-the-art or competitive performance, demonstrating particular strength in out-of-distribution scenarios and when relying solely on unlabeled pretraining data.

Abstract

Learning transferable representations for electroencephalography (EEG) remains challenging because EEG signals are inherently multi-channel and non-stationary. Channels observed at the same time provide coupled measurements of neural activity, while the relevant temporal dynamics vary across contexts. This structure is poorly matched by architectures that apply uniform computation across time or route each channel patch independently. To this end, we propose TRACE, an autoregressive EEG pre-training framework that predicts future EEG patches from causal context while performing temporally adaptive and cross-channel coherent computation. At each temporal step, TRACE derives an expert routing decision from the causal cross-channel history and applies it jointly to all channels at that step. This preserves instantaneous cross-channel coherence while allowing different temporal regimes to activate different computation. Since routing is defined over the available channel set and causal temporal context, TRACE is compatible with heterogeneous pre-training across corpora with different channel counts, montages, sequence lengths, and recording domains. Across eight downstream EEG benchmarks, TRACE is evaluated in both settings: when downstream domains are seen only as unlabeled pre-training data and when downstream datasets are completely unseen during pre-training. It obtains the best results on several benchmarks while remaining competitive on motor imagery and clinical event classification tasks, with ablations supporting the importance of cross-channel temporal routing.

Problem

• EEG representation learning
• multi-channel signals
• non-stationarity
• temporal dynamics
• cross-channel coherence

Innovation

• Temporal Routing
• Autoregressive Modeling
• Cross-channel Coherence
• EEG Representation Learning
• Expert Mixture