Multi-Head Noise Regression for Single-Channel EEG: Estimating Ocular and Muscle Contamination to Guide Artifact Removal

Electroencephalography (EEG) is often contaminated by ocular electrooculographic (EOG) and muscle electromyographic (EMG) artifacts, yet many pipelines apply uniform denoising, risking distortion of clean neural activity. We propose a two-head, single-channel regressor that estimates EOG and EMG noise-to-signal ratio (NSR, dB) from short segments and test whether it can guide selective artifact reduction, including downstream brain–computer interface decoding.

Using EEGdenoiseNet clean EEG and artifact exemplars, we synthesized 2 s single-channel mixtures with known EOG/EMG NSR spanning −10 to +10 dB and trained several model families to jointly regress both NSRs. Generalization was evaluated on an independent eyeblink dataset via agreement with regression-based ocular-reference topographies, and in two applications: (i) gating stationary wavelet blink removal on a P3 event-related potential (ERP) dataset and (ii) gating the same denoiser on a 55-subject RSVP P300 speller dataset (FP1/FP2).

A dilated temporal convolutional network (TCN) performed best (EOG: MAE ≈ 1.8 dB, R2 ≈ 0.82; EMG: MAE ≈ 1.0 dB, R2 ≈ 0.94) with low bias across NSR. The EOG head recovered blink topographies (median spatial correlation ≈ 0.91). On the P3 dataset, indiscriminate wavelet denoising reduced significant ERP channels, whereas TCN-guided gating preserved 22–23 of 24 while processing ∼9%–20% of segments. On the speller dataset, denoising all epochs reduced decoding, while selective denoising improved AUC (θ = 9 dB: ΔAUC = 0.327, p = 0.0040) while denoising 12.45 ± 9.29% of test segments.

Multi-head noise regression provides interpretable, continuous ocular and muscle contamination estimates that can act as control signals for conservative, noise-aware artifact handling under constrained-lead conditions.