Counteracting Detrimental Propagation across Space and Time in EEG

Electroencephalography (EEG) enables non-invasive, real-time recording of neural activity, with broad applications in neurological diagnosis, monitoring and rehabilitation. Yet, reliable EEG analysis is hindered by task-irrelevant signals that propagate across electrodes, accumulate over time, and become entangled with predictive cues. Existing attempts focus on the outcome level by regularizing resulting representations or structures, leaving the propagation process largely unaddressed. By the time they intervene, detrimental signals may already have spread and become entangled with predictive cues, making post-hoc removal intrinsically difficult and risking collateral damage to task-relevant neural signatures. In this paper, we propose CounterField, which recasts the mitigation of task-irrelevant signals as counteraction against their propagation from a graph stochastic partial differential equation (SPDE) perspective. CounterField structures a spectral response against detrimental propagation, enforcing coherent counteraction while preserving predictive flow. We extensively evaluate CounterField across diverse EEG tasks, where consistent gains demonstrate the effectiveness of propagation-level counteraction in learning compact yet predictive spatiotemporal dynamics.