Preprint: Position-Adaptive Time Scheduling for EEG Generation

Summary A preprint proposes an adaptive EEG generation framework based on conditional flow matching to ease data scarcity in brain-computer interfaces and support large-scale neural modeling. Noting that existing flow methods assume one global time course across all channels and time segments, the framework adds position-adaptive time scheduling that tracks per-position reconstruction error to modulate each position's time course, plus decomposed spatiotemporal attention and a frequency-aligned multi-resolution spectral consistency loss to model cross-channel dependencies and compensate for EEG's power-law spectral bias. Across three EEG datasets with different acquisition protocols and task semantics, it consistently beat the strongest baselines, cutting TS-FID by up to 62.2% and lifting downstream classification accuracy by up to 6.77 percentage points. The study has not been peer reviewed.