Abstract:Electroencephalography (EEG) acquisition is often affected by large-amplitude variations across subjects and sessions. This makes conventional front ends vulnerable to saturation and clipping. Modulo sampling addresses this limitation by folding the signal into a bounded range rather than truncating it. The recovery task is then to reconstruct the original waveform from periodic observations, which is a highly ill-posed inverse problem. In this work, we propose a compact signal-structured framework for modulo EEG recovery. The method combines phase-aware features, graph-temporal modeling, and structured fold-state decoding. A boundary-guided gating technique improves inference near wrap events, where uncertainty is highest. Experiments across two EEG datasets, over a wide range of folding thresholds, demonstrate consistent recovery performance. Ablation and noise studies further support the contribution of the structured decoding technique. The proposed signal-processing approach effectively recovers folded EEG recordings. These results show that structured fold-state decoding is effective for recovering folded EEG. The code is accessible at this https URL.
From: Soujanya Hazra [view email]
[v1]
Thu, 30 Oct 2025 17:47:56 UTC (182 KB)
[v2]
Thu, 20 Aug 2026 19:40:47 UTC (1,668 KB)
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