# Prior-Guided Frequency-Calibrated Virtual EEG Channel Inference from Four Frontal Electrodes for Wearable EEG Augmentation

arXiv: https://arxiv.org/abs/2605.29263 · HTML: https://mlsanity.com/paper/2605.29263/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 28 May 2026.

## Builds on (most cited first, 3 of 3)

- [ESTformer: Transformer utilising spatiotemporal dependencies for electroencephalogram super-resolution](https://mlsanity.com/paper/2312.10052/) (2023)
- [Deep EEG Super-Resolution: Upsampling EEG Spatial Resolution with Generative Adversarial Networks](https://mlsanity.com/paper/2502.08803/) (2025)
- [You Only Acquire Sparse-channel (YOAS): A Unified Framework for Dense-channel EEG Generation](https://mlsanity.com/paper/2406.15269/) (2024)

## Abstract (arXiv)

Low-channel wearable electroencephalography (EEG) is attractive for long-term monitoring, but four frontal electrodes provide only a sparse and spatially biased sampling of the scalp potential field. Virtual-channel methods should therefore be framed not as recovery of independent unmeasured brain activity, but as prior-guided conditional inference of posterior predictive scalp-potential representations at target electrode locations. We present FAVC-Net, a compact frequency-calibrated virtual-channel inference network that estimates 13 target channels from Fp1, Fp2, F7, and F8. The model combines shared multi-scale source encoding, source-state embeddings, target-conditioned signed source-block mixing, GATv2-based attention refinement, attention-consistent skip fusion, and weak Welch power spectral density calibration. The generator is trained as a task-agnostic reconstruction module, without class-label, classification, or CSP-like discriminative constraints, so that the virtual montage remains tied to conditional scalp-potential estimation rather than to a specific downstream decision. On the PRED+CT dataset, FAVC-Net achieved the best joint waveform-spectral operating point among neural and interpolation baselines. Its time-domain gains were modest, whereas log-spectral distance and PSD KL divergence were reduced by 30.50% and 38.94% relative to the strongest non-FAVC comparator. Under wearable-like source perturbations, the model preserved spectral fidelity and channel-frequency texture, with anti-collapse benefits most evident under EMG-like bursts and mixed stress. These results support virtual EEG channels as montage-compatible, frequency-calibrated posterior predictive representations derived from sparse frontal measurements, not as independent substitutes for physically recorded electrodes.