{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T21:17:01Z","timestamp":1776979021085,"version":"3.51.4"},"reference-count":54,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T00:00:00Z","timestamp":1750982400000},"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. Neurosci."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Dry electroencephalography (EEG) allows for recording cortical activity in ecological scenarios with a high channel count, but it is often more prone to artifacts as compared to gel-based EEG. Spatial harmonic analysis (SPHARA) and ICA-based methods (Fingerprint and ARCI) have been separately used in previous studies for dry EEG de-noising and physiological artifact reduction. Here, we investigate if the combination of these techniques further improves EEG signal quality. For this purpose, we also introduced an improved version of SPHARA.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Dry 64-channel EEG was recorded from 11 healthy volunteers during a motor performance paradigm (left and right hand, feet, and tongue movements). EEG signals were denoised separately using Fingerprint + ARCI, SPHARA, a combination of these two methods, and a combination of these two methods including an improved SPHARA version. The improved version of SPHARA includes an additional zeroing of artifactual jumps in single channels before application of SPHARA. The EEG signal quality after application of each denoising method was calculated by means of standard deviation (SD), signal to noise ratio (SNR), and root mean square deviation (RMSD), and a generalized linear mixed effects (GLME) model was used to identify significant changes of these parameters and quantify the changes in the EEG signal quality.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The grand average values of SD improved from 9.76 (reference preprocessed EEG) to 8.28, 7.91, 6.72, and 6.15 \u03bcV for Fingerprint + ARCI, SPHARA, Fingerprint + ARCI + SPHARA, and Fingerprint + ARCI + improved SPHARA, respectively. Similarly, the RMSD values improved from 4.65 to 4.82, 6.32, and 6.90 \u03bcV, and the SNR values changed from 2.31 to 1.55, 4.08, and 5.56 dB.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Our results demonstrate the different performance aspects of Fingerprint + ARCI and SPHARA, artifact reduction and de-noising techniques that complement each other. We also demonstrated that a combination of these techniques yields superior performance in the reduction of artifacts and noise in dry EEG recordings, which can be extended to infant EEG and adult MEG applications.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnins.2025.1576954","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T12:54:01Z","timestamp":1751028841000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Combination of spatial and temporal de-noising and artifact reduction techniques in multi-channel dry EEG"],"prefix":"10.3389","volume":"19","author":[{"given":"Milana","family":"Komosar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gabriella","family":"Tamburro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Uwe","family":"Graichen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Silvia","family":"Comani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jens","family":"Haueisen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,6,27]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"407","DOI":"10.2478\/v10175-012-0052-3","article-title":"ICA-Based EEG denoising: A comparative analysis of fifteen methods.","volume":"60","author":"Albera","year":"2012","journal-title":"Bull. Pol. Acad. Sci. Tech. Sci."},{"key":"B2","doi-asserted-by":"crossref","DOI":"10.1109\/MeMeA54994.2022.9856433","article-title":"Comparing artifact removal techniques for daily-life electroencephalography with few channels","author":"Arpaia","year":"2022","journal-title":"Proceedings of the 2022 IEEE international symposium on medical measurements and applications (MeMeA)"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2105.12916","article-title":"Robust learning from corrupted EEG with dynamic spatial filtering","author":"Banville","year":"2021","journal-title":"arXiv"},{"key":"B4","doi-asserted-by":"crossref","DOI":"10.1109\/BIBM55620.2022.9995132","article-title":"Automatic removal of scalp EEG artifacts using an interpretable hybrid deep learning method","author":"Bao","year":"2022","journal-title":"Proceedings of the 2022 IEEE international conference on bioinformatics and biomedicine (BIBM)"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1162\/neco.1995.7.6.1129","article-title":"An information-maximization approach to blind separation and blind deconvolution.","volume":"7","author":"Bell","year":"1995","journal-title":"Neural Comput."},{"key":"B6","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1152\/jn.00560.2019","article-title":"Local spatial analysis: An easy-to-use adaptive spatial EEG filter.","volume":"125","author":"Bufacchi","year":"2021","journal-title":"J. Neurophysiol."},{"key":"B7","doi-asserted-by":"publisher","first-page":"118809","DOI":"10.1016\/j.neuroimage.2021.118809","article-title":"A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiology.","volume":"247","author":"Cohen","year":"2021","journal-title":"NeuroImage"},{"key":"B8","doi-asserted-by":"publisher","first-page":"809227","DOI":"10.3389\/fnins.2021.809227","article-title":"Editorial: Dry electroencephalography for brain monitoring in sports and movement science.","volume":"15","author":"Comani","year":"2021","journal-title":"Front. Neurosci."},{"key":"B9","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1109\/TNSRE.2014.2346621","article-title":"FORCe: Fully online and automated artifact removal for brain-computer interfacing.","volume":"23","author":"Daly","year":"2015","journal-title":"IEEE Trans. Neural. Syst. Rehabil. Eng."},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1101\/541318","article-title":"Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research.","author":"Das","year":"2019","journal-title":"bioRxiv [preprint]"},{"key":"B11","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.jneumeth.2007.09.012","article-title":"Sensor noise suppression.","volume":"168","author":"de Cheveign\u00e9","year":"2008","journal-title":"J. Neurosci. Methods"},{"key":"B12","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","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":"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":"108961","DOI":"10.1016\/j.jneumeth.2020.108961","article-title":"Integrated Platform Lossless (EEG-IP-L) pre-processing pipeline for objective signal quality assessment incorporating data annotation and blind source separation.","volume":"347","author":"Desjardins","year":"2021","journal-title":"J. Neurosci. Methods"},{"key":"B15","doi-asserted-by":"publisher","first-page":"982","DOI":"10.3389\/fnins.2019.00982","article-title":"Dry EEG in sports sciences: A fast and reliable tool to assess individual alpha peak frequency changes induced by physical effort.","volume":"13","author":"di Fronso","year":"2019","journal-title":"Front. Neurosci."},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2303.06019","article-title":"Scatter-based common spatial patterns \u2013 a unified spatial filtering framework.","author":"Dong","year":"2023","journal-title":"arXiv [Preprint]"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1338","DOI":"10.1002\/ima.22680","article-title":"Hybrid time-reassigned multisynchrosqueezing transform -Picard -based automated electroencephalography artifact correction methodology for brain\u2013computer interface applications.","volume":"32","author":"Dovedi","year":"2022","journal-title":"Int. J. Imag. Syst. Technol."},{"key":"B18","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.1002\/hbm.25721","article-title":"A high-density 256-channel cap for dry electroencephalography.","volume":"43","author":"Fiedler","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"B19","doi-asserted-by":"publisher","first-page":"9745","DOI":"10.3390\/s23249745","article-title":"Simultaneous dry and gel-based high-density electroencephalography recordings.","volume":"23","author":"Fiedler","year":"2023","journal-title":"Sensors (Basel)"},{"key":"B20","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s10548-015-0435-5","article-title":"Novel multipin electrode cap system for dry electroencephalography.","volume":"28","author":"Fiedler","year":"2015","journal-title":"Brain Topogr."},{"key":"B21","doi-asserted-by":"publisher","first-page":"48","DOI":"10.3389\/fnins.2018.00048","article-title":"Group-Level EEG-Processing pipeline for flexible single trial-based analyses including linear mixed models.","volume":"12","author":"Fr\u00f6mer","year":"2018","journal-title":"Front. Neurosci."},{"key":"B22","doi-asserted-by":"publisher","first-page":"011004","DOI":"10.1088\/1741-2552\/ac542c","article-title":"Removal of movement-induced EEG artifacts: Current state of the art and guidelines.","volume":"19","author":"Gorjan","year":"2022","journal-title":"J. Neural Eng."},{"key":"B23","doi-asserted-by":"publisher","first-page":"e0121741","DOI":"10.1371\/journal.pone.0121741","article-title":"SPHARA\u2013a generalized spatial fourier analysis for multi-sensor systems with non-uniformly arranged sensors: Application to EEG.","volume":"10","author":"Graichen","year":"2015","journal-title":"PLoS One"},{"key":"B24","doi-asserted-by":"publisher","first-page":"100289","DOI":"10.1016\/j.softx.2019.100289","article-title":"SpharaPy: A python toolbox for spatial harmonic analysis of non-uniformly sampled data.","volume":"10","author":"Graichen","year":"2019","journal-title":"SoftwareX"},{"key":"B25","doi-asserted-by":"crossref","DOI":"10.1109\/CISP-BMEI51763.2020.9263595","article-title":"Improved adaptive filtering based artifact removal from EEG signals","author":"Hua","year":"2020","journal-title":"Proceedings of the 2020 13th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)"},{"key":"B26","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":"B27","doi-asserted-by":"publisher","first-page":"987","DOI":"10.3390\/s19050987","article-title":"Removal of artifacts from EEG signals: A review.","volume":"19","author":"Jiang","year":"2019","journal-title":"Sensors (Basel)"},{"key":"B28","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1111\/1469-8986.3720163","article-title":"Removing electroencephalographic artifacts by blind source separation.","volume":"37","author":"Jung","year":"2000","journal-title":"Psychophysiology"},{"key":"B29","doi-asserted-by":"publisher","first-page":"056027","DOI":"10.1088\/1741-2552\/ab2b61","article-title":"Characterization and real-time removal of motion artifacts from EEG signals.","volume":"16","author":"Kilicarslan","year":"2019","journal-title":"J. Neural Eng."},{"key":"B30","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-74918-7","author":"Kn\u00f6sche","year":"2022","journal-title":"EEG\/MEG source reconstruction: Textbook for electro-and magnetoencephalography."},{"key":"B31","doi-asserted-by":"publisher","first-page":"26","DOI":"10.3390\/brainsci7030026","article-title":"Application of linear mixed-effects models in human neuroscience research: A comparison with pearson correlation in two auditory electrophysiology studies.","volume":"7","author":"Koerner","year":"2017","journal-title":"Brain Sci."},{"key":"B32","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1515\/cdbme-2022-1066","article-title":"Bad channel detection in EEG recordings.","volume":"8","author":"Komosar","year":"2022","journal-title":"Curr. Direct. Biomed. Eng."},{"key":"B33","first-page":"45","article-title":"Automatic identification and removal of ocular artifacts from EEG using wavelet transform.","volume":"6","author":"Krishnaveni","year":"2006","journal-title":"Measurement Sci. Rev."},{"key":"B34","doi-asserted-by":"publisher","first-page":"1002","DOI":"10.1109\/TBME.2017.2734641","article-title":"Reducing sensor noise in MEG and EEG recordings using oversampled temporal projection.","volume":"65","author":"Larson","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"B35","doi-asserted-by":"publisher","first-page":"giz002","DOI":"10.1093\/gigascience\/giz002","article-title":"EEG dataset and OpenBMI toolbox for three BCI paradigms: An investigation into BCI illiteracy.","volume":"8","author":"Lee","year":"2019","journal-title":"Gigascience"},{"key":"B36","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1162\/089976699300016719","article-title":"Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources.","volume":"11","author":"Lee","year":"1999","journal-title":"Neural Comput."},{"key":"B37","doi-asserted-by":"publisher","first-page":"8","DOI":"10.25122\/jml-2019-0085","article-title":"The role of quantitative EEG in the diagnosis of neuropsychiatric disorders.","volume":"13","author":"Livint Popa","year":"2020","journal-title":"J. Med. Life"},{"key":"B38","doi-asserted-by":"publisher","first-page":"30630","DOI":"10.1109\/ACCESS.2018.2842082","article-title":"Identification and removal of physiological artifacts from electroencephalogram signals: A review.","volume":"6","author":"Mannan","year":"2018","journal-title":"IEEE Access"},{"key":"B39","doi-asserted-by":"publisher","first-page":"35","DOI":"10.46604\/peti.2021.7653","article-title":"Artifact removal methods in EEG recordings: A review.","volume":"20","author":"Mathe","year":"2021","journal-title":"Proc. Eng. Technol. Innov."},{"key":"B40","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/s12021-018-9397-6","article-title":"Intracerebral EEG artifact identification using convolutional neural networks.","volume":"17","author":"Nejedly","year":"2019","journal-title":"Neuroinformatics"},{"key":"B41","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.neuroimage.2005.12.003","article-title":"Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks.","volume":"31","author":"Pfurtscheller","year":"2006","journal-title":"Neuroimage"},{"key":"B42","doi-asserted-by":"publisher","first-page":"1087976","DOI":"10.3389\/fnint.2023.1087976","article-title":"Spatiotemporal phase slip patterns for visual evoked potentials, covert object naming tasks, and insight moments extracted from 256 channel EEG recordings.","volume":"17","author":"Ramon","year":"2023","journal-title":"Front. Integr. Neurosci."},{"key":"B43","doi-asserted-by":"publisher","first-page":"354","DOI":"10.19101\/IJATEE.2021.874883","article-title":"EEG artifacts detection and removal techniques for brain computer interface applications: A systematic review.","volume":"9","author":"Rashmi","year":"2022","journal-title":"Int. J. Adv. Technol. Eng. Exp."},{"key":"B44","doi-asserted-by":"crossref","DOI":"10.5772\/intechopen.102698","article-title":"Effective EEG artifact removal from EEG signal","author":"Roy","year":"2022","journal-title":"Biosignal processing"},{"key":"B45","doi-asserted-by":"publisher","first-page":"685513","DOI":"10.3389\/felec.2021.685513","article-title":"Motion artifact removal techniques for wearable EEG and PPG sensor systems.","volume":"2","author":"Seok","year":"2021","journal-title":"Front. Electron."},{"key":"B46","doi-asserted-by":"publisher","first-page":"102373","DOI":"10.1016\/j.bspc.2020.102373","article-title":"SWT-kurtosis based algorithm for elimination of electrical shift and linear trend from EEG signals.","volume":"65","author":"Shahbakhti","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"B47","doi-asserted-by":"publisher","first-page":"96","DOI":"10.3389\/fnhum.2018.00096","article-title":"Automatic removal of physiological artifacts in EEG: The optimized fingerprint method for sports science applications.","volume":"12","author":"Stone","year":"2018","journal-title":"Front. Hum. Neurosci."},{"key":"B48","doi-asserted-by":"publisher","first-page":"918","DOI":"10.1109\/TITB.2012.2207400","article-title":"A methodology for validating artifact removal techniques for physiological signals.","volume":"16","author":"Sweeney","year":"2012","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"B49","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":"B50","doi-asserted-by":"publisher","first-page":"441","DOI":"10.3389\/fnins.2019.00441","article-title":"Automatic Removal of Cardiac Interference (ARCI): A new approach for EEG data.","volume":"13","author":"Tamburro","year":"2019","journal-title":"Front. Neurosci."},{"key":"B51","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1051\/matecconf\/201713500058","article-title":"Artefacts removal of EEG signals with wavelet denoising.","volume":"135","author":"Turnip","year":"2017","journal-title":"MATEC Web Conf."},{"key":"B52","doi-asserted-by":"publisher","first-page":"031001","DOI":"10.1088\/1741-2560\/12\/3\/031001","article-title":"EEG artifact removal-state-of-the-art and guidelines.","volume":"12","author":"Urig\u00fcen","year":"2015","journal-title":"J. Neural Eng."},{"key":"B53","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.jphysparis.2009.07.003","article-title":"Improving the SNR of EEG generated by deep sources with weighted multielectrode leads.","volume":"103","author":"V\u00e4is\u00e4nen","year":"2009","journal-title":"J. Physiol. Paris"},{"key":"B54","doi-asserted-by":"publisher","first-page":"e37665","DOI":"10.1371\/journal.pone.0037665","article-title":"Translation of EEG spatial filters from resting to motor imagery using independent component analysis.","volume":"7","author":"Wang","year":"2012","journal-title":"PLoS One"}],"container-title":["Frontiers in Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnins.2025.1576954\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T12:54:04Z","timestamp":1751028844000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnins.2025.1576954\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,27]]},"references-count":54,"alternative-id":["10.3389\/fnins.2025.1576954"],"URL":"https:\/\/doi.org\/10.3389\/fnins.2025.1576954","relation":{},"ISSN":["1662-453X"],"issn-type":[{"value":"1662-453X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,27]]},"article-number":"1576954"}}