Shaikh, Usman QamarKalra, AnubhaLowe, AndrewNiazi, Imran Khan2026-03-262026-03-262026-03-18Journal of Neural Engineering, ISSN: 1741-2560 (Print); 1741-2552 (Online), IOP Publishing. doi: 10.1088/1741-2552/ae541d1741-25601741-2552http://hdl.handle.net/10292/20822EEG is often contaminated by ocular (EOG) and muscle (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 BCI decoding.
Approach. Using EEGdenoiseNet clean EEG and artifact exemplars, we synthesised 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. Generalisation 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 ERP dataset and (ii) gating the same denoiser on a 55-subject RSVP P300 speller dataset (FP1/FP2).
Main results. 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.
Significance. 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.&#xD.As the Version of Record of this article is going to be / has been published on a gold open access basis under a CC BY 4.0 licence, this Accepted Manuscript is available for reuse under a CC BY 4.0 licence immediately. Everyone is permitted to use all or part of the original content in this article, provided that they adhere to all the terms of the licence https://creativecommons.org/licences/by/4.0. Although reasonable endeavours have been taken to obtain all necessary permissions from third parties to include their copyrighted content within this article, their full citation and copyright line may not be present in this Accepted Manuscript version. Before using any content from this article, please refer to the Version of Record on IOPscience once published for full citation and copyright details, as permissions may be required. All third party content is fully copyright protected and is not published on a gold open access basis under a CC BY licence, unless that is specifically stated in the figure caption in the Version of Record.EEGartifact removalsingle channel40 Engineering3208 Medical Physiology32 Biomedical and Clinical Sciences4003 Biomedical EngineeringNeurosciences0903 Biomedical Engineering1103 Clinical Sciences1109 NeurosciencesBiomedical Engineering3209 Neurosciences4003 Biomedical engineeringJournal ArticleOpenAccess10.1088/1741-2552/ae541d