CAFE: Channel-Autoregressive Factorized Encoding for Robust Biosignal Spatial Super-Resolution

TL;DR

CAFE introduces a novel, plug-and-play autoregressive method for spatial super-resolution of biosignals, effectively reconstructing full montages from low-density data with improved accuracy and generality across modalities and models.

Contribution

The paper presents CAFE, a new autoregressive framework that enhances biosignal spatial super-resolution by exploiting local structures and reducing artifacts, with broad applicability and improved performance.

Findings

Outperforms five baseline methods in reconstruction quality.

Demonstrates robustness across four modalities and six datasets.

Works with multiple backbone architectures including MLP, Conv, and Transformer.

Abstract

High-density biosignal recordings are critical for neural decoding and clinical monitoring, yet real-world deployments often rely on low-density (LD) montages due to hardware and operational constraints. This motivates spatial super-resolution from LD observations, but heterogeneous dependencies under sparse and noisy measurements often lead to artifact propagation and false non-local correlations. To address this, we propose CAFE, a plug-and-play rollout generation scheme that reconstructs the full montage in geometry-aligned stages. Starting from the LD channels, CAFE first recovers nearby channels and then progressively expands to more distal regions, exploiting reliable local structure before introducing non-local interactions. During training, step-wise supervision is applied over channel groups and teacher forcing with epoch-level scheduled sampling along the group dimension is utilized to reduce exposure bias, enabling parallel computation across steps. At test time, CAFE performs an autoregressive rollout across groups, while remaining plug-and-play by reusing any temporal backbone as the shared predictor. Evaluated on $4$ modalities and $6$ datasets, CAFE demonstrates plug-and-play generality across $3$ backbones (MLP, Conv, Transformer) and achieves consistently better reconstruction than $5$ representative baselines.