{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:10:43Z","timestamp":1792793443908,"version":"4.5.0"},"reference-count":53,"publisher":"IOP Publishing","issue":"3","license":[{"start":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T00:00:00Z","timestamp":1750896000000},"content-version":"vor","delay-in-days":25,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T00:00:00Z","timestamp":1750896000000},"content-version":"tdm","delay-in-days":25,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Stratton VA Medical Center","id":[{"id":"https:\/\/ror.org\/01rpj9v06","id-type":"ROR","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["1R01NS102646-01A1"],"award-info":[{"award-number":["1R01NS102646-01A1"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Neural Eng."],"published-print":{"date-parts":[[2025,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    . We present a novel and lightweight method that removes ocular artifacts from electroencephalography (EEG) recordings while demanding minimal training data.\n                    <jats:italic>Approach<\/jats:italic>\n                    . A robust, cross-validated thresholding procedure automatically detects the times at which eye blinks occur, then a linear scalp projection is estimated by regressing a simplified time-locked reference signal against the multi-channel EEG.\n                    <jats:italic>Main results<\/jats:italic>\n                    . Performance was compared against four commonly-used and readily available blink removal methods: signal subspace projection and forward regression (Reg) from the MNE toolbox, EEGLab\u2019s independent component analysis (ICA) combined with ICLabel for automated component identification, and Artifact Subspace Reconstruction (ASR) Python implementation compatible with MNE. On semi-synthetic blink-contaminated EEG data, our method exhibited better reconstruction of the ground truth than the two MNE native methods, and comparable (or better in some scenarios) performance to ASR algorithm and ICA+IClabel. We also examined a real EEG dataset from 16 human participants, where the ground truth was unknown. Our method affected contaminated channels in blink intervals more than the two MNE native methods and ASR, while having a smaller impact on non-blink intervals, uncontaminated channels, and higher-frequency amplitudes, than the two MNE methods; its performance was again similar to ICA+ICLabel. On a second real dataset from 42 human participants, we showed that ARMBR removed the unwanted blink artifacts while successfully preserving the desired event-related-potential signals.\n                    <jats:italic>Significance<\/jats:italic>\n                    . The proposed algorithm exhibited comparable, and in some scenarios better performance relative to readily-available implementations of other widely-used methods. Another feature of our method is its potential as method for online applications. Therefore, it stands to make valuable contributions towards the automation of neural-engineering technologies and their translation from laboratory to clinical and other real-world usage.\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/ade566","type":"journal-article","created":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:50:48Z","timestamp":1750186248000},"page":"036048","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Artifact-reference multivariate backward regression (ARMBR): a novel method for EEG blink artifact removal with minimal data requirements"],"prefix":"10.1088","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4317-701X","authenticated-orcid":true,"given":"L","family":"Alkhoury","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9336-8792","authenticated-orcid":false,"given":"G","family":"Scanavini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S","family":"Louviot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A","family":"Radanovic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2720-4479","authenticated-orcid":false,"given":"S A","family":"Shah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4253-6976","authenticated-orcid":true,"given":"N J","family":"Hill","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,6,26]]},"reference":[{"key":"jneade566bib1","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.knosys.2013.02.014","type":"journal-article","article-title":"Automated EEG analysis of epilepsy: a review","volume":"45","author":"Acharya","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"jneade566bib2","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1111\/opo.12131","type":"journal-article","article-title":"When to use the Bonferroni correction","volume":"34","author":"Armstrong","year":"2014","journal-title":"Ophthal. Physiol. Opt."},{"key":"jneade566bib3","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1162\/neco.1995.7.6.1129","type":"journal-article","article-title":"An information-maximization approach to blind separation and blind deconvolution","volume":"7","author":"Bell","year":"1995","journal-title":"Neural Comput."},{"key":"jneade566bib4","first-page":"pp 1","author":"Benesty","year":"2009","type":"book"},{"key":"jneade566bib5","article-title":"Headcaps Products","author":"BioSemi","type":"other"},{"key":"jneade566bib6","doi-asserted-by":"publisher","first-page":"141","DOI":"10.3389\/fnhum.2019.00141","type":"journal-article","article-title":"A Riemannian modification of artifact subspace reconstruction for EEG artifact handling","volume":"13","author":"Blum","year":"2019","journal-title":"Front. Hum. Neurosci."},{"key":"jneade566bib7","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1016\/j.cub.2018.01.080","type":"journal-article","article-title":"Electrophysiological correlates of semantic dissimilarity reflect the comprehension of natural, narrative speech","volume":"28","author":"Broderick","year":"2018","journal-title":"Curr. Biol."},{"key":"jneade566bib8","doi-asserted-by":"publisher","DOI":"10.5061\/dryad.070jc)","type":"journal-article","article-title":"Data from: electrophysiological correlates of semantic dissimilarity reflect the comprehension of natural, narrative speech","author":"Broderick","year":"2019","journal-title":"Dryad"},{"key":"jneade566bib9","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.jneumeth.2006.05.033","type":"journal-article","article-title":"Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis","volume":"158","author":"Castellanos","year":"2006","journal-title":"J. Neurosci. Methods"},{"key":"jneade566bib10","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/S0987-7053(00)00055-1","type":"journal-article","article-title":"Removal of ocular artifact from the EEG: a review","volume":"30","author":"Croft","year":"2000","journal-title":"Neurophysiol. Clin.\/Clin. Neurophysiol."},{"key":"jneade566bib11","doi-asserted-by":"publisher","first-page":"604","DOI":"10.3389\/fnhum.2016.00604","type":"journal-article","article-title":"The multivariate temporal response function (MTRF) toolbox: a matlab toolbox for relating neural signals to continuous stimuli","volume":"10","author":"Crosse","year":"2016","journal-title":"Front. Hum. Neurosci."},{"key":"jneade566bib12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2023.3342863","type":"journal-article","article-title":"A dual-branch interactive fusion network to remove artifacts from single-channel EEG","volume":"73","author":"Cui","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"jneade566bib13","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","type":"journal-article","article-title":"EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis","volume":"134","author":"Delorme","year":"2004","journal-title":"J. Neurosci. Methods"},{"key":"jneade566bib14","article-title":"asrpy","author":"DiGyt","type":"other"},{"key":"jneade566bib15","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1177\/1550059419875916","type":"journal-article","article-title":"Quantitative and qualitative EEG as a prediction tool for outcome and complications in acute stroke patients","volume":"51","author":"Doerrfuss","year":"2020","journal-title":"Clin. EEG Neurosci."},{"key":"jneade566bib16","doi-asserted-by":"publisher","first-page":"giac015","DOI":"10.1093\/gigascience\/giac015","type":"journal-article","article-title":"The nencki-symfonia electroencephalography\/event-related potential dataset: Multiple cognitive tasks and resting-state data collected in a sample of healthy adults","volume":"11","author":"Dzianok","year":"2022","journal-title":"GigaScience"},{"key":"jneade566bib17","first-page":"pp 1","type":"conference-proceedings","article-title":"Automatic detection and correction of blink artifacts in single channel neural EEG signals","author":"Goru","year":"2020"},{"key":"jneade566bib18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fnins.2013.00267","type":"journal-article","article-title":"MEG and EEG data analysis with MNE-Python","volume":"7","author":"Gramfort","year":"2013","journal-title":"Front. Neurosci."},{"key":"jneade566bib19","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1016\/0013-4694(83)90135-9","type":"journal-article","article-title":"A new method for off-line removal of ocular artifact","volume":"55","author":"Gratton","year":"1983","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"jneade566bib20","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.neubiorev.2013.09.015","type":"journal-article","article-title":"EEG-neurofeedback for optimising performance. I: a review of cognitive and affective outcome in healthy participants","volume":"44","author":"Gruzelier","year":"2014","journal-title":"Neurosci. Biobehav. Rev."},{"key":"jneade566bib21","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.neuroimage.2013.10.067","type":"journal-article","article-title":"On the interpretation of weight vectors of linear models in multivariate neuroimaging","volume":"87","author":"Haufe","year":"2014","journal-title":"NeuroImage"},{"key":"jneade566bib22","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/BF02344717","type":"journal-article","article-title":"Removal of ocular artifacts from electro-encephalogram by adaptive filtering","volume":"42","author":"He","year":"2004","journal-title":"Med. Biol. Eng. Comput."},{"key":"jneade566bib23","article-title":"Time-dependent demixing of task-relevant EEG sources","author":"Hill","year":"2006","type":"conference-proceedings"},{"key":"jneade566bib24","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1111\/1469-8986.3720163","type":"journal-article","article-title":"Removing electroencephalographic artifacts by blind source separation","volume":"37","author":"Jung","year":"2000","journal-title":"Psychophysiology"},{"key":"jneade566bib25","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/13\/2\/026013","type":"journal-article","article-title":"A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements","volume":"13","author":"Kilicarslan","year":"2016","journal-title":"J. Neural Eng."},{"key":"jneade566bib26","article-title":"Artifact removal techniques with signal reconstruction","author":"Kothe","year":"2016","type":"other"},{"key":"jneade566bib27","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1088\/0967-3334\/27\/4\/008","type":"journal-article","article-title":"Automatic removal of the eye blink artifact from eeg using an ICA-based template matching approach","volume":"27","author":"Li","year":"2006","journal-title":"Physiol. Meas."},{"key":"jneade566bib28","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-90437-7","type":"journal-article","article-title":"Eye-blink artifact removal from single channel EEG with k-Means and SSA","volume":"11","author":"Maddirala","year":"2021","journal-title":"Sci. Rep."},{"key":"jneade566bib29","doi-asserted-by":"publisher","first-page":"1361","DOI":"10.1109\/TNSRE.2022.3176575","type":"journal-article","article-title":"ICA With CWT and k-Means for eye-blink artifact removal from fewer channel EEG","volume":"30","author":"Maddirala","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"jneade566bib30","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1109\/JBHI.2014.2333010","type":"journal-article","article-title":"Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy, kurtosis and wavelet-ICA","volume":"19","author":"Mahajan","year":"2015","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"jneade566bib31","doi-asserted-by":"publisher","first-page":"p 8","DOI":"10.5555\/2998828.2998849","type":"conference-proceedings","article-title":"Independent component analysis of electroencephalographic data","author":"Makeig","year":"1995"},{"key":"jneade566bib32","doi-asserted-by":"publisher","first-page":"6553","DOI":"10.3390\/e16126553","type":"journal-article","article-title":"Enhanced automatic wavelet independent component analysis for electroencephalographic artifact removal","volume":"16","author":"Mammone","year":"2014","journal-title":"Entropy"},{"key":"jneade566bib33","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1016\/j.neuroimage.2017.11.062","type":"journal-article","article-title":"Eeg microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: a review","volume":"180","author":"Michel","year":"2018","journal-title":"Neuroimage"},{"key":"jneade566bib34","article-title":"Repairing artifacts with regression","author":"MNE 1.4 Documentation","type":"other"},{"key":"jneade566bib35","article-title":"Repairing artifacts with SSP","author":"MNE 1.4 Documentation","type":"other"},{"key":"jneade566bib36","article-title":"mne.preprocessing.computeprojeog","author":"MNE 1.4 Documentation","type":"other"},{"key":"jneade566bib37","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1111\/j.1469-8986.2010.01061.x","type":"journal-article","article-title":"ADJUST: an automatic EEG artifact detector based on the joint use of spatial and temporal features","volume":"48","author":"Mognon","year":"2011","journal-title":"Psychophysiology"},{"key":"jneade566bib38","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1109\/TNSRE.2016.2641956","type":"journal-article","article-title":"A passive EEG-BCI for single-trial detection of changes in mental state","volume":"25","author":"Myrden","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"jneade566bib39","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1525\/9780520339897-003","type":"book","author":"Neyman","year":"1928"},{"key":"jneade566bib40","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.neuroimage.2019.05.026","type":"journal-article","article-title":"ICLabel: an automated electroencephalographic independent component classifier, dataset and website","volume":"198","author":"Pion-Tonachini","year":"2019","journal-title":"NeuroImage"},{"key":"jneade566bib41","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1111\/j.1439-054X.2006.00101.x","type":"journal-article","article-title":"A review of sleep EEG patterns. Part I: a compilation of amended rules for their visual recognition according to rechtschaffen and kales","volume":"10","author":"Rodenbeck","year":"2006","journal-title":"Somnologie"},{"key":"jneade566bib42","author":"Schomer","year":"2017","type":"book"},{"key":"jneade566bib43","first-page":"pp 1","type":"conference-proceedings","article-title":"Removal of blink from EEG by empirical mode decomposition (EMD)","author":"Shahbakhti","year":"2012"},{"key":"jneade566bib44","article-title":"EOG artifact removal from single and multi-channel EEG recordings through the combination of long short-term memory networks and independent component analysis","author":"TaghiBeyglou","year":"2023","type":"preprint"},{"key":"jneade566bib45","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/BF02534144","type":"journal-article","article-title":"Signal-space projection method for separating MEG or EEG into components","volume":"35","author":"Uusitalo","year":"1997","journal-title":"Med. Biol. Eng. Comput."},{"key":"jneade566bib46","first-page":"pp 196","type":"book","article-title":"Individual comparisons by ranking methods","author":"Wilcoxon","year":"1992"},{"key":"jneade566bib47","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1016\/S1388-2457(02)00057-3","type":"journal-article","article-title":"Brain\u2013computer interfaces for communication and control","volume":"113","author":"Wolpaw","year":"2002","journal-title":"Clin. Neurophysiol."},{"key":"jneade566bib48","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2020.108918","type":"journal-article","article-title":"A comprehensive assessment of brain computer interfaces: Recent trends and challenges","volume":"346","author":"Yadav","year":"2020","journal-title":"J. Neurosci. Methods"},{"key":"jneade566bib49","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac63eb","type":"journal-article","article-title":"Embedding decomposition for artifacts removal in EEG signals","volume":"19","author":"Yu","year":"2022","journal-title":"J. Neural Eng."},{"key":"jneade566bib50","doi-asserted-by":"publisher","first-page":"14839","DOI":"10.3390\/s131114839","type":"journal-article","article-title":"EOG artifact correction from EEG recording using stationary subspace analysis and empirical mode decomposition","volume":"13","author":"Zeng","year":"2013","journal-title":"Sensors"},{"key":"jneade566bib51","first-page":"pp 1265","type":"conference-proceedings","article-title":"A novel convolutional neural network model to remove muscle artifacts from EEG","author":"Zhang","year":"2021"},{"key":"jneade566bib52","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac2bf8","type":"journal-article","article-title":"EEGdenoiseNet: a benchmark dataset for deep learning solutions of EEG denoising","volume":"18","author":"Zhang","year":"2021","journal-title":"J. Neural Eng."},{"key":"jneade566bib53","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1109\/TCYB.2018.2797176","type":"journal-article","article-title":"Emotionmeter: a multimodal framework for recognizing human emotions","volume":"49","author":"Zheng","year":"2018","journal-title":"IEEE Trans. Cybern."}],"container-title":["Journal of Neural Engineering"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ade566","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ade566\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ade566","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ade566\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ade566\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ade566\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ade566\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ade566\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T17:03:13Z","timestamp":1761066193000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ade566"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,1]]},"references-count":53,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,6,26]]},"published-print":{"date-parts":[[2025,6,1]]}},"URL":"https:\/\/doi.org\/10.1088\/1741-2552\/ade566","relation":{},"ISSN":["1741-2560","1741-2552"],"issn-type":[{"value":"1741-2560","type":"print"},{"value":"1741-2552","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,1]]},"assertion":[{"value":"Artifact-reference multivariate backward regression (ARMBR): a novel method for EEG blink artifact removal with minimal data requirements","name":"article_title","label":"Article Title"},{"value":"Journal of Neural Engineering","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2025 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2023-10-25","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-06-17","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-06-26","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}