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Neural Eng."],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Single-channel and wearable electroencephalography (EEG) reduce acquisition burden but limit the spatial information available to conventional multichannel artifact-removal methods. We conducted a systematic scoping review of single-channel-compatible EEG artifact removal, linking method operation with artifact coverage, evaluation design, generalization, deployment, comparison, and reproducibility.\n                    <jats:italic>Approach.<\/jats:italic>\n                    Database and citation searches through 15 2026 identified 178 articles describing 199 method variants. For learned methods, 303 reported model-dataset evaluations were also examined for data independence, generalization, and deployment.\n                    <jats:italic>Main results.<\/jats:italic>\n                    Most variants were reference-free at inference (179\/199), while 13 used a non-neural auxiliary reference. Methods spanned seven operating paradigms, including decomposition, transformed source separation, adaptive correction, and learned reconstruction. Ocular and muscle artifacts were evaluated most often, and 38\/178 articles tested simultaneous or overlapping mixtures. Across 380 dataset uses, evaluation centered on controlled signal reconstruction. Known-target fidelity was reported for 170\/199 variants, compared with 21 for clean-input or non-artifact signal comparison, 32 for contaminated downstream utility, and three for clean-input downstream non-degradation. Among 81 learned variants, 18 reported source-trained application to an external dataset, device, or site; only 31\/303 evaluations explicitly separated test windows or epochs, clean sources, and artifact sources from training. No measured deployment evidence was reported for 122\/199 variants. Public method resources were available for 21 variants, while trained weights and explicit licenses were uncommon.\n                    <jats:italic>Significance.<\/jats:italic>\n                    The review provides a framework for selecting datasets, metrics, comparators, and implementations, and for distinguishing reconstruction, neural preservation, transfer, and wearable deployment evidence\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/aeb0ce","type":"journal-article","created":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T22:50:43Z","timestamp":1791327043000},"page":"051001","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Single-channel EEG denoising and artifact removal: a systematic scoping review of methods, benchmarking, and reproducibility"],"prefix":"10.1088","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0171-3940","authenticated-orcid":true,"given":"Usman Qamar","family":"Shaikh","sequence":"first","affiliation":[{"name":"Auckland University of Technology"},{"name":"School of Engineering Computer and Mathematical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7908-3053","authenticated-orcid":true,"given":"Anubha Manju","family":"Kalra","sequence":"additional","affiliation":[{"name":"Auckland University of Technology"},{"name":"School of Engineering Computer and Mathematical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0612-1036","authenticated-orcid":false,"given":"Andrew","family":"Lowe","sequence":"additional","affiliation":[{"name":"Auckland University of Technology"},{"name":"Auckland University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8752-7224","authenticated-orcid":false,"given":"Imran Khan","family":"Niazi","sequence":"additional","affiliation":[{"name":"New Zealand College of Chiropractic"},{"name":"Aalborg University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,10,15]]},"reference":[{"key":"jneaeb0cebib1","doi-asserted-by":"publisher","first-page":"5770","DOI":"10.3390\/s25185770","type":"journal-article","article-title":"A Systematic Review of Techniques for Artifact Detection and Artifact Category Identification in Electroencephalography from Wearable Devices","volume":"25","author":"Arpaia","year":"2025","journal-title":"Sensors"},{"key":"jneaeb0cebib2","doi-asserted-by":"publisher","DOI":"10.3389\/felec.2021.685513","type":"journal-article","article-title":"Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems","volume":"2","author":"Seok","year":"2021","journal-title":"Front. 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