RIGS-EEG: Does Reference-Invariant Graph-Spectral Reconstruction Improve Missing EEG Channel Recovery?

Whole-channel loss removes local temporal observations from EEG, making reconstruction depend on information from other electrodes. Common-reference components and high-amplitude observations can affect spatial propagation, while temporal fitting alone provides insufficient constraints on missing potentials. We introduce RIGS-EEG, a five-step unrolled network that combines common-reference separation, reliability-weighted spatial initialization, and graph–time-frequency shrinkage. The model represents relative potentials on an electrode graph, progressively refines estimates through lightweight spectral gains, and exactly preserves observed samples at the output, using only 970 learnable parameters. With a fixed mask and reliability weights invariant to common shifts, the architecture is equivariant to additive common-reference signals. We evaluate 25%, 50%, and 75% whole-channel missingness on PhysioNet, SEED-IV, and MCD against 14 baseline implementations. At 75% missingness, mean NRMSE is 0.0723, 0.1274, and 0.0922, respectively. MCD evaluation uses held-out subjects and averages three recording stages within each subject. With a fixed downstream classifier and 25% missing channels, reconstruction improves balanced accuracy by 3.44–8.37 percentage points and Cohen’s kappa by 0.068–0.122 over corrupted inputs across four PhysioNet movement and motor-imagery tasks. Ablations support the contributions of spatial and spectral constraints and the benefit of reliability weighting on SEED-IV. These findings support explicitly organizing reference, geometry, and spectral information within a compact reconstruction model to recover incomplete EEG while retaining information useful for downstream recognition.