DECODE: Dual-Enhanced Conditioned Diffusion for EEG Forecasting
This work addresses a fundamental problem in neuroscience and Brain-Computer Interfaces by enabling more accurate and interpretable prediction of neural activity, with potential for zero-shot generalization to novel behaviors.
The paper tackles the challenge of forecasting EEG signals during cognitive events by introducing DECODE, a framework that combines semantic guidance from natural language with temporal dynamics to generate event-specific neural responses, achieving sub-microvolt prediction accuracy (MAE = 0.626 microvolt) over 75 timestep horizons on a driving task dataset.
Forecasting Electroncephalography (EEG) signals during cognitive events remains a fundamental challenge in neuroscience and Brain-Computer Interfaces (BCIs), as existing methods struggle to capture both the stochastic nature of neural dynamics and the semantic context of behavioral tasks. We present the Dual-Enhanced COnditioned Diffusion (DECODE) for EEG, a novel framework that unifies semantic guidance from natural language descriptions with temporal dynamics from historical signals to generate event-specific neural responses. DECODE leverages pre-trained language models to condition the diffusion process on rich textual descriptions of cognitive events, while maintaining temporal coherence through history-based Langevin dynamics. Evaluated on a real-world driving task dataset with five distinct behaviors, DECODE achieves sub-microvolt prediction accuracy (MAE = 0.626 microvolt) over 75 timestep horizons while maintaining well-calibrated uncertainty estimates. Our framework demonstrates that natural language can effectively bridge high-level cognitive descriptions and low-level neural dynamics, opening new possibilities for zero-shot generalization to novel behaviors and interpretable BCIs. By generating physiologically plausible, event-specific EEG trajectories conditioned on semantic descriptions, DECODE establishes a new paradigm for understanding and predicting context-dependent neural activity.