{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T17:35:41Z","timestamp":1790876141888,"version":"4.1.0"},"reference-count":107,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T00:00:00Z","timestamp":1697587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Hum. Neurosci."],"abstract":"<jats:p>Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscle activity, fasciculation, cable swings, or magnetic induction pose significant challenges in real-world BCI applications. In this paper, we present a systematic review of methods for motion artifact reduction in online BCI experiments. Using the PRISMA filter method, we conducted a comprehensive literature search on PubMed, focusing on open access publications from 1966 to 2022. We evaluated 2,333 publications based on predefined filtering rules to identify existing methods and pipelines for motion artifact reduction in EEG data. We present a lookup table of all papers that passed the defined filters, all used methods, and pipelines and compare their overall performance and suitability for online BCI experiments. We summarize suitable methods, algorithms, and concepts for motion artifact reduction in online BCI applications, highlight potential research gaps, and discuss existing community consensus. This review aims to provide a comprehensive overview of the current state of the field and guide researchers in selecting appropriate methods for motion artifact reduction in online BCI experiments.<\/jats:p>","DOI":"10.3389\/fnhum.2023.1251690","type":"journal-article","created":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T11:29:50Z","timestamp":1697628590000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review"],"prefix":"10.3389","volume":"17","author":[{"given":"Mathias","family":"Schmoigl-Tonis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph","family":"Schranz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gernot R.","family":"M\u00fcller-Putz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,10,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1007\/s11517-020-02252-3","article-title":"The influence of motor tasks and cut-off parameter selection on artifact subspace reconstruction in EEG recordings","volume":"58","author":"Anders","year":"2020","journal-title":"Med. Biol. Eng. Comp"},{"key":"B2","doi-asserted-by":"publisher","first-page":"e0197153","DOI":"10.1371\/journal.pone.0197153","article-title":"Performance-based approach for movement artifact removal from electroencephalographic data recorded during locomotion","volume":"13","author":"Arad","year":"2018","journal-title":"PLoS ONE"},{"key":"B3","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1049\/htl2.12016","article-title":"Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering","volume":"8","author":"Beach","year":"2021","journal-title":"Healthc. Technol. Lett"},{"key":"B4","doi-asserted-by":"crossref","DOI":"10.1145\/1553374.1553380","article-title":"\u201cCurriculum learning,\u201d","volume-title":"International Conference on Machine Learning","author":"Bengio","year":"2009"},{"key":"B5","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1002\/hbm.21448","article-title":"Mapping hemodynamic correlates of seizures using fmri: a review","volume":"34","author":"Chaudhary","year":"2011","journal-title":"Hum. Brain Mapp"},{"key":"B6","doi-asserted-by":"publisher","first-page":"18370","DOI":"10.3390\/s141018370","article-title":"A preliminary study of muscular artifact cancellation in single-channel EEG","volume":"14","author":"Chen","year":"2014","journal-title":"Sensors"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2018.00680","article-title":"A decoding scheme for incomplete motor imagery EEG with deep belief network","author":"Chu","year":"2018","journal-title":"Front. Neurosci"},{"key":"B8","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.jneumeth.2016.01.005","article-title":"Sparse time artifact removal","volume":"262","author":"d Cheveign\u00e9","year":"2016","journal-title":"J. Neurosci. Methods"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.3390\/e23091170","article-title":"A fast approach to removing muscle artifacts for EEG with signal serialization based ensemble empirical mode decomposition","author":"Dai","year":"2021","journal-title":"Entropy"},{"key":"B10","doi-asserted-by":"publisher","first-page":"2725","DOI":"10.3390\/s17122725","article-title":"Adaptive spatial filter based on similarity indices to preserve the neural information on EEG signals during on-line processing","volume":"17","author":"Delisle-Rodriguez","year":"2017","journal-title":"Sensors"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1101\/2022.12.03.518987","article-title":"Eeg is better left alone","author":"Delorme","year":"2022","journal-title":"bioRxiv"},{"key":"B12","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","article-title":"EEG LAB: 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":"B13","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1016\/j.neuroimage.2006.11.004","article-title":"Enhanced detection of artifacts in eeg data using higher-order statistics and independent component analysis","volume":"34","author":"Delorme","year":"2007","journal-title":"Neuroimage"},{"key":"B14","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1080\/01621459.1983.10477973","article-title":"Estimating the error rate of a prediction rule: improvement on cross-validation","volume":"78","author":"Efron","year":"1983","journal-title":"J. Am. Stat. Assoc"},{"key":"B15","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1214\/088342304000000396","article-title":"Permutation methodsand: a basis for exact inference","volume":"19","author":"Ernst","year":"2004","journal-title":"Stat. Sci"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1007\/s40279-021-01598-4","article-title":"Upper-body post activation performance enhancement for athletic performance: a systematic review with meta-analysis and recommendations for future research","author":"Finlay","year":"2022","journal-title":"Sports Med"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1016\/j.dcn.2022.101077","article-title":"Automated pipeline for infants continuous EEG (APICE): a flexible pipeline for developmental cognitive studies","author":"Fl\u00f3","year":"2022","journal-title":"Dev. Cogn. Neurosci"},{"key":"B18","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s40708-017-0074-6","article-title":"Removal of muscular artifacts in EEG signals: a comparison of linear decomposition methods","volume":"5","author":"Fr\u00f8lich","year":"2018","journal-title":"Brain Informatics"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2011.00208","article-title":"The use of electroencephalography in language production research: a review","author":"Ganushchak","year":"2011","journal-title":"Front. Psychol"},{"key":"B20","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.jneumeth.2015.08.019","article-title":"Informed decomposition of electroencephalographic data","volume":"256","author":"Gordon","year":"2015","journal-title":"J. Neurosci. Methods"},{"key":"B21","doi-asserted-by":"publisher","first-page":"601","DOI":"10.3390\/s19030601","article-title":"Quality assessment of single-channel EEG for wearable devices","volume":"19","author":"Grosselin","year":"2019","journal-title":"Sensors"},{"key":"B22","doi-asserted-by":"publisher","first-page":"3526","DOI":"10.1152\/jn.00105.2010","article-title":"Removal of movement artifact from high-density EEG recorded during walking and running","volume":"103","author":"Gwin","year":"2010","journal-title":"J. Neurophysiol"},{"key":"B23","doi-asserted-by":"publisher","first-page":"180074","DOI":"10.1038\/sdata.2018.74","article-title":"A mobile brain-body imaging dataset recorded during treadmill walking with a bci","volume":"5","author":"He","year":"2018","journal-title":"Sci Data"},{"key":"B24","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/S0893-6080(00)00026-5","article-title":"Independent component analysis: algorithms and applications","volume":"13","author":"Hyv\u00e4rinen","year":"2000","journal-title":"Neural Netw"},{"key":"B25","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1097\/00004691-200307000-00004","article-title":"Independent component analysis as a tool to eliminate artifacts in EEG: a quantitative study","volume":"20","author":"Iriarte","year":"2003","journal-title":"J. Clin. Neurophysiol"},{"key":"B26","doi-asserted-by":"publisher","first-page":"e0242857","DOI":"10.1371\/journal.pone.0242857","article-title":"Applications of eeg indices for the quantification of human cognitive performance: a systematic review and bibliometric analysis","volume":"15","author":"Ismail","year":"2020","journal-title":"PLoS ONE"},{"key":"B27","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.neuroimage.2017.06.030","article-title":"Autoreject: Automated artifact rejection for MEG and EEG data","volume":"159","author":"Jas","year":"2017","journal-title":"Neuroimage"},{"key":"B28","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1002\/hbm.1050","article-title":"Analysis and visualization of single-trial event-related potentials","volume":"14","author":"Jung","year":"2001","journal-title":"Hum. Brain Mapp"},{"key":"B29","doi-asserted-by":"publisher","first-page":"7711","DOI":"10.3390\/s21227711","article-title":"The influence of filters on EEG-ERP testing: analysis of motor cortex in healthy subjects","volume":"21","author":"Karpiel","year":"2021","journal-title":"Sensors"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2020.613254","article-title":"Analysis of human gait using hybrid eeg-fnirs-based bci system: a review","author":"Khan","year":"2021","journal-title":"Front. Hum. Neurosci"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/12\/4\/046022","article-title":"Isolating gait-related movement artifacts in electroencephalography during human walking","author":"Kline","year":"2015","journal-title":"J. Neural Eng"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.31234\/osf.io\/yq3vj","article-title":"The potential of functional near-infrared spectroscopy-based neurofeedback\u2014a systematic review and recommendations for best practice","author":"Kohl","year":"2020","journal-title":"Front. Neurosci"},{"key":"B33","doi-asserted-by":"publisher","first-page":"e0112392","DOI":"10.1371\/journal.pone.0112392","article-title":"The user-centered design as novel perspective for evaluating the usability of bci-controlled applications","volume":"9","author":"K\u00fcler","year":"2014","journal-title":"PLoS ONE"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1016\/j.dcn.2022.101068","article-title":"NEAR: an artifact removal pipeline for human newborn EEG data","author":"Kumaravel","year":"2022","journal-title":"Dev. Cogn. Neurosci"},{"key":"B35","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.jneumeth.2012.05.017","article-title":"Detection and classification of subject-generated artifacts in EEG signals using autoregressive models","volume":"208","author":"Lawhern","year":"2012","journal-title":"J. Neurosci. Methods"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2018.00943","article-title":"An approach for brain-controlled prostheses based on a facial expression paradigm","author":"Li","year":"2018","journal-title":"Front. Neuroscie"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.597941","article-title":"Electromyogram (EMG) removal by adding sources of EMG (ERASE)\u2014 novel ICA-based algorithm for removing myoelectric artifacts from EEG","author":"Li","year":"2021","journal-title":"Front. Neurosci"},{"key":"B38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/4159676","article-title":"Removal of EMG artifacts from multichannel EEG signals using combined singular spectrum analysis and canonical correlation analysis","volume":"2019","author":"Liu","year":"2019","journal-title":"J. Healthc. Eng"},{"key":"B39","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1109\/TIM.2019.2906967","article-title":"Improving EEG muscle artifact removal with an EMG array","volume":"69","author":"Mucarquer","year":"2020","journal-title":"IEEE Trans. Instrum. Meas"},{"key":"B40","doi-asserted-by":"publisher","first-page":"2302","DOI":"10.3390\/s19102302","article-title":"An unsupervised method for artefact removal in EEG signals","volume":"19","author":"Mur","year":"2019","journal-title":"Sensors"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2013.00138","article-title":"High-frequency brain activity and muscle artifacts in meg\/eeg: a review and recommendations","author":"Muthukumaraswamy","year":"2013","journal-title":"Front. Hum. Neurosci"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2021.566004","article-title":"Quantifying signal quality from unimodal and multimodal sources: application to EEG with ocular and motion artifacts","author":"Nahmias","year":"2021","journal-title":"Front. Neurosci"},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2015.00708","article-title":"Negligible motion artifacts in scalp electroencephalography (EEG) during treadmill walking","author":"Nathan","year":"2016","journal-title":"Front. Hum. Neurosci"},{"key":"B44","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1016\/S0079-6123(06)59025-9","article-title":"Motor imagery and eeg-based control of spelling devices and neuroprostheses","volume":"159","author":"Neuper","year":"2006","journal-title":"Elsevier"},{"key":"B45","author":"Nicolas-Alonso","year":"2012","journal-title":"Brain Computer Interfaces: A Review"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-41131-2","article-title":"Human electrocortical dynamics while stepping over obstacles","author":"Nordin","year":"2019","journal-title":"Sci. Rep"},{"key":"B47","doi-asserted-by":"publisher","first-page":"842","DOI":"10.1109\/TBME.2019.2921766","article-title":"Faster gait speeds reduce alpha and beta EEG spectral power from human sensorimotor cortex","volume":"67","author":"Nordin","year":"2020","journal-title":"IEEE Transact. Biomed. Eng"},{"key":"B48","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1159\/000382023","article-title":"State-of-the-art analysis of high-frequency (gamma range) electroencephalography in humans","volume":"72","author":"Nottage","year":"2015","journal-title":"Neuropsychobiology"},{"key":"B49","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2016.00188","article-title":"Proposing metrics for benchmarking novel EEG technologies towards real-world measurements","author":"Oliveira","year":"2016","journal-title":"Front. Hum. Neurosci"},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2017.00225","article-title":"A channel rejection method for attenuating motion-related artifacts in EEG recordings during walking","author":"Oliveira","year":"2017","journal-title":"Front. Neurosci"},{"key":"B51","doi-asserted-by":"publisher","DOI":"10.1186\/s40708-020-00109-x","article-title":"Technological advancements and opportunities in neuromarketing: a systematic review","author":"Rawnaque","year":"2020","journal-title":"Brain Informat"},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2021.611962","article-title":"Adaptive filtering for improved EEG-based mental workload assessment of ambulant users","author":"Rosanne","year":"2021","journal-title":"Front. Neurosci"},{"key":"B53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/9674712","article-title":"Gaussian elimination-based novel canonical correlation analysis method for EEG motion artifact removal","volume":"2017","author":"Roy","year":"2017","journal-title":"J. Healthc. Eng"},{"key":"B54","doi-asserted-by":"publisher","first-page":"608920","DOI":"10.3389\/fdgth.2020.608920","article-title":"Unsupervised EEG artifact detection and correction","volume":"2","author":"Saba-Sadiya","year":"2021","journal-title":"Front. Digital Health"},{"key":"B55","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1049\/htl.2019.0053","article-title":"Effective automated method for detection and suppression of muscle artefacts from single-channel EEG signal","volume":"7","author":"Saini","year":"2020","journal-title":"Healthc. Technol. Lett"},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2018.00654","article-title":"A method for simultaneous evaluation of muscular and neural prepulse inhibition","author":"San-Martin","year":"2018","journal-title":"Front. Neurosci"},{"key":"B57","article-title":"\u201cOnline artifact reduction and sequential evidence accumulation enhances robustness of thought-based interaction,\u201d","volume-title":"6th International Brain-Computer Interface Conference","author":"Scherer","year":"2014"},{"key":"B58","doi-asserted-by":"publisher","first-page":"e0201900","DOI":"10.1371\/journal.pone.0201900","article-title":"Suppression of overlearning in independent component analysis used for removal of muscular artifacts from electroencephalographic records","volume":"13","author":"Sebek","year":"2018","journal-title":"PLoS ONE"},{"key":"B59","article-title":"\u201cReconstructing gait cycle patterns from non-invasive recorded low gamma modulations,\u201d","volume-title":"6th International Brain-Computer Interface Conference","author":"Seeber","year":"2014"},{"key":"B60","article-title":"Toward Brain-Computer Interfacing","volume-title":"(Neural Information Processing)","author":"Sejnowski","year":"2007"},{"key":"B61","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1007\/s10548-009-0079-4","article-title":"Electromyogenic artifacts and electroencephalographic inferences","volume":"54","author":"Shackman","year":"2009","journal-title":"Neuroimage"},{"key":"B62","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/0304-3959(84)90128-3","article-title":"Chronic phantom and stump pain among american veterans: results of a survey","volume":"18","author":"Sherman","year":"1984","journal-title":"Pain"},{"key":"B63","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2015.00639","article-title":"Independent component analysis of gait-related movement artifact recorded using EEG electrodes during treadmill walking","author":"Snyder","year":"2015","journal-title":"Front. Human Neurosci"},{"key":"B64","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2021.720229","article-title":"A toolbox and crowdsourcing platform for automatic labeling of independent components in electroencephalography","author":"Soghoyan","year":"2021","journal-title":"Front. Neuroinform"},{"key":"B65","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.3390\/s18041073","article-title":"Effects of cable sway, electrode surface area, and electrode mass on electroencephalography signal quality during motion","volume":"18","author":"Symeonidou","year":"2018","journal-title":"Sensors"},{"key":"B66","doi-asserted-by":"publisher","first-page":"e4380","DOI":"10.7717\/peerj.4380","article-title":"A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings","volume":"6","author":"Tamburro","year":"2018","journal-title":"PeerJ"},{"key":"B67","article-title":"\u201cUnderstanding self-supervised learning with dual deep networks\u201d.","author":"Tian","year":"2020","journal-title":"International Conference on Machine Learning"},{"key":"B68","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.3390\/e23081030","article-title":"Choosing strategies to deal with artifactual EEG data in children with cognitive impairment","volume":"23","author":"Tost","year":"2021","journal-title":"Entropy"},{"key":"B69","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2019.00080","article-title":"Optimization of real-time EEG artifact removal and emotion estimation for human-robot interaction applications","author":"Val-Calvo","year":"2019","journal-title":"Front. Comput. Neurosci"},{"key":"B70","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1146\/annurev.bb.02.060173.001105","article-title":"Toward direct brain-computer communication","volume":"2","author":"Vidal","year":"1973","journal-title":"Annu. Rev. Biophys. Bioeng"},{"key":"B71","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac123f","article-title":"Improving motor imagery classification during induced motor perturbations","author":"Vidaurre","year":"2021","journal-title":"J. Neural Eng"},{"key":"B72","article-title":"\u201cActive participation during walking reduces single trial connectivity in sensorimotor areas,\u201d","volume-title":"6th International Brain-Computer Interface Conference","author":"Wagner","year":"2014"},{"key":"B73","doi-asserted-by":"publisher","DOI":"10.1186\/1744-9081-7-30","article-title":"Automatic classification of artifactual ICA-components for artifact removal in EEG signals","author":"Winkler","year":"2011","journal-title":"Behav. Brain Funct"},{"key":"B74","doi-asserted-by":"publisher","first-page":"170","DOI":"10.3389\/fnhum.2017.00170","article-title":"Neuroimaging of human balance control: a systematic review","volume":"11","author":"Wittenberg","year":"2017","journal-title":"Front. Hum. Neurosci."},{"key":"B75","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1016\/S1388-2457(02)00057-3","article-title":"Brain-computer interfaces for communication and control","volume":"113","author":"Wolpaw","year":"2002","journal-title":"Clin. Neurophysiol"},{"key":"B76","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2014.00504","article-title":"Interference of tonic muscle activity on the EEG: a single motor unit study","author":"Yilmaz","year":"2014","journal-title":"Front. Hum. Neurosci"},{"key":"B77","doi-asserted-by":"publisher","DOI":"10.1186\/1743-0003-9-50","article-title":"Automatic artefact removal in a self-paced hybrid brain- computer interface system","author":"Yong","year":"2012","journal-title":"J. Neuroeng. Rehabil"},{"key":"B78","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/720450","article-title":"Automatic artifact removal from electroencephalogram data based on a priori artifact information","author":"Zhang","year":"2015","journal-title":"BioMed Res. Int"},{"key":"B79","doi-asserted-by":"publisher","first-page":"3051","DOI":"10.3390\/s22083051","article-title":"Improved cognitive vigilance assessment after artifact reduction with wavelet independent component analysis","volume":"22","author":"Abu-Farha","year":"2022","journal-title":"Sensors"},{"key":"B80","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci9120347","article-title":"Peak detection with online electroencephalography (EEG) artifact removal for brain\u2013computer interface (BCI) purposes","author":"Benda","year":"2019","journal-title":"Brain Sci"},{"key":"B81","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2019.00141","article-title":"A riemannian modification of artifact subspace reconstruction for EEG artifact handling","author":"Blum","year":"2019","journal-title":"Front. Hum. Neurosci"},{"key":"B82","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2014\/374679","article-title":"Dual adaptive filtering by optimal projection applied to filter muscle artifacts on EEG and comparative study","volume":"2014","author":"Boudet","year":"2014","journal-title":"Sci. World J"},{"key":"B83","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2014.00376","article-title":"Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution","author":"Bulea","year":"2014","journal-title":"Front. Neurosci"},{"key":"B84","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2016.00060","article-title":"Characterization of artifacts produced by gel displacement on non-invasive brain-machine interfaces during ambulation","author":"Costa","year":"2016","journal-title":"Front. Neurosci"},{"key":"B85","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1111\/psyp.12810","article-title":"Localized component filtering for electroencephalogram artifact rejection","volume":"54","author":"DelPozo-Ba\u00f1os","year":"2017","journal-title":"Psychophysiology"},{"key":"B86","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2018.00097","article-title":"The harvard automated processing pipeline for electroencephalography (HAPPE): standardized processing software for developmental and high-artifact data","author":"Gabard-Durnam","year":"2018","journal-title":"Front. Neurosci"},{"key":"B87","doi-asserted-by":"publisher","first-page":"3169","DOI":"10.3390\/s22093169","article-title":"Motion artifacts correction from single-channel EEG and fNIRS signals using novel wavelet packet decomposition in combination with canonical correlation analysis","volume":"22","author":"Hossain","year":"2022","journal-title":"Sensors"},{"key":"B88","doi-asserted-by":"publisher","first-page":"465","DOI":"10.32598\/bcn.2021.1388.2","article-title":"EEG artifact removal system for depression using a hybrid denoising approach","volume":"12","author":"Kaur","year":"2021","journal-title":"Basic Clin. Neurosci. J"},{"key":"B89","doi-asserted-by":"publisher","DOI":"10.1186\/1743-0003-9-47","article-title":"Weighted phase lag index stability as an artifact resistant measure to detect cognitive EEG activity during locomotion","author":"Lau","year":"2012","journal-title":"J. Neuroeng. Rehabil"},{"key":"B90","doi-asserted-by":"publisher","first-page":"e0062944","DOI":"10.1371\/journal.pone.0062944","article-title":"DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals","volume":"8","author":"Lawhern","year":"2013","journal-title":"PLoS ONE"},{"key":"B91","doi-asserted-by":"publisher","first-page":"e13566","DOI":"10.1111\/psyp.13566","article-title":"Adjusting ADJUST: optimizing the ADJUST algorithm for pediatric data using geodesic nets","volume":"57","author":"Leach","year":"2020","journal-title":"Psychophysiology"},{"key":"B92","doi-asserted-by":"publisher","DOI":"10.1186\/1475-925X-12-77","article-title":"L1 norm based common spatial patterns decomposition for scalp EEG BCI","author":"Li","year":"2013","journal-title":"Biomed. Eng. Online"},{"key":"B93","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/5081258","article-title":"Real-time EEG signal enhancement using canonical correlation analysis and gaussian mixture clustering","author":"Lin","year":"2018","journal-title":"J. Healthc. Eng"},{"key":"B94","doi-asserted-by":"crossref","DOI":"10.1109\/EMBC.2015.7320216","article-title":"\u201cUse of multiscale entropy to facilitate artifact detection in electroencephalographic signals,\u201d","volume-title":"2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","author":"Mariani","year":"2015"},{"key":"B95","doi-asserted-by":"publisher","first-page":"2416","DOI":"10.1016\/j.neuroimage.2009.10.010","article-title":"Validation of ICA-based myogenic artifact correction for scalp and source-localized EEG","volume":"49","author":"McMenamin","year":"2010","journal-title":"Neuroimage"},{"key":"B96","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1111\/j.1469-8986.2009.00787.x","article-title":"Validation of regression-based myogenic correction techniques for scalp and source-localized EEG","volume":"46","author":"McMenamin","year":"2009","journal-title":"Psychophysiology"},{"key":"B97","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.jneumeth.2016.04.015","article-title":"Robust power spectral estimation for EEG data","volume":"268","author":"Melman","year":"2016","journal-title":"J. Neurosci. Methods"},{"key":"B98","doi-asserted-by":"crossref","DOI":"10.1109\/ISBI.2009.5193005","article-title":"\u201cGeneralized sidelobe canceller for magnetoencephalography arrays,\u201d","volume-title":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","author":"Mosher","year":"2009"},{"key":"B99","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1007\/s10548-012-0255-9","article-title":"A novel method for reducing the effect of tonic muscle activity on the gamma band of the scalp EEG","volume":"26","author":"Nottage","year":"2012","journal-title":"Brain Topogr"},{"key":"B100","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.jneumeth.2015.01.030","article-title":"EEG artifact elimination by extraction of ICA-component features using image processing algorithms","volume":"243","author":"Rad\u00fcntz","year":"2015","journal-title":"J. Neurosci. Methods"},{"key":"B101","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2014.00156","article-title":"Methodological aspects of EEG and body dynamics measurements during motion","author":"Reis","year":"2014","journal-title":"Front. Hum. Neurosci"},{"key":"B102","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/8432953","article-title":"Wavelet-based semblance methods to enhance the single-trial detection of event-related potentials for a BCI spelling system","author":"Saavedra","year":"2019","journal-title":"Comp. Intell. Neurosci"},{"key":"B103","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2018.00096","article-title":"Automatic removal of physiological artifacts in EEG: the optimized fingerprint method for sports science applications","author":"Stone","year":"2018","journal-title":"Front. Hum. Neurosci"},{"key":"B104","doi-asserted-by":"publisher","DOI":"10.1002\/adhm.201700552","article-title":"Stretchable dry electrodes with concentric ring geometry for enhancing spatial resolution in electrophysiology","author":"Wang","year":"2017","journal-title":"Adv. Healthc. Mater"},{"key":"B105","doi-asserted-by":"publisher","first-page":"30","DOI":"10.12688\/f1000research.10569.2","article-title":"AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software","volume":"6","author":"Weiss","year":"2017","journal-title":"F1000Research"},{"key":"B106","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-18503-8","article-title":"Fully organic compliant dry electrodes self-adhesive to skin for long-term motion-robust epidermal biopotential monitoring","author":"Zhang","year":"2020","journal-title":"Nat. Commun"},{"key":"B107","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1109\/JBHI.2014.2370646","article-title":"Automatic identification of artifact-related independent components for artifact removal in EEG recordings","volume":"20","author":"Zou","year":"2016","journal-title":"IEEE J. Biomed. Health Informat"}],"container-title":["Frontiers in Human Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnhum.2023.1251690\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T11:29:55Z","timestamp":1697628595000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnhum.2023.1251690\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,18]]},"references-count":107,"alternative-id":["10.3389\/fnhum.2023.1251690"],"URL":"https:\/\/doi.org\/10.3389\/fnhum.2023.1251690","relation":{},"ISSN":["1662-5161"],"issn-type":[{"value":"1662-5161","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,18]]},"article-number":"1251690"}}