Shared Structure, Individual Dynamics: Flow Matching for EEG Generation

Clinical electroencephalography (EEG) generation requires both patient-shared signal structure and sample-specific temporal detail. We study how the source distribution and training objective can address these requirements in Flow Matching. We propose GEMIA, a conditional Flow Matching method with a Global Structured Endpoint (GSE) and Multi-Scale Increment Alignment. GSE estimates spectral–spatial structure with equal weight for each training patient and models waveform shape and amplitude separately. The network predicts the clean endpoint while the transport loss is in velocity space; to limit endpoint error amplification, we cap the time-dependent weight in the velocity-\(L_1\) loss. Auxiliary time-domain losses complement the structured source by aligning per-channel scale, target temporal increments at several lags, and waveform correlation. We evaluate seven generators on four patient-wise TUH protocols, assessing signal fidelity, downstream utility, and memorization. GEMIA achieves the lowest mean TS-FID on all four protocols, with reductions of \(4.7%\)–\(63.1%\) relative to JET. It also achieves lower mean TS-FID with five Heun steps than the Flow Matching baselines with fifty steps. Source-objective ablations show that the benefit of GSE depends on the accompanying loss. These results support source-objective co-design for EEG generation.