SiDe: Long-Sequence Electrophysiological Generation with Raw-Signal Detail Recovery

Electrophysiological recordings often become long sequences due to long temporal spans or high sampling rates. Yet most biosignal generation studies remain in short-window settings, and scaling them to long sequences is not a simple extrapolation. Without compression, modeling a large number of raw time points in the full signal space quickly becomes computationally expensive; with front-end latent compression, high-frequency statistics and local signal details may be suppressed before generation begins. We study where compression should occur: long-range structure can be modeled in compressed tokens, but the final raw-space velocity should not be forced to recover local detail only through these tokens. We propose SiDe, a Signal-space Detailer that keeps compression in a conditional Transformer backbone while providing an output-side raw-signal path for velocity prediction. On pose-conditioned EMG generation, SiDe reduces rFID from 7.363 to 2.414 compared with a JiT-style baseline, and better preserves frequency profiles and common EMG statistical feature distributions. The generated EMG also follows pose-speed manipulations and matches real-EMG degradation trends under controlled perturbations. Additional experiments on Sleep-EDF EEG and PTB-XL ECG further characterize where raw-signal detail recovery helps across modalities.