{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T17:45:04Z","timestamp":1793382304085,"version":"4.5.0"},"reference-count":49,"publisher":"IOP Publishing","issue":"1","license":[{"start":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T00:00:00Z","timestamp":1738627200000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/iopscience.iop.org\/page\/copyright"},{"start":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T00:00:00Z","timestamp":1738627200000},"content-version":"tdm","delay-in-days":3,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61904038"],"award-info":[{"award-number":["61904038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Key Research and Development Program of China","award":["2021YFC0122700"],"award-info":[{"award-number":["2021YFC0122700"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2021SHZDZX0103"],"award-info":[{"award-number":["2021SHZDZX0103"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Neural Eng."],"published-print":{"date-parts":[[2025,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Denoising artifacts, such as noise from muscle or cardiac activity, is a crucial and ubiquitous concern in neurophysiological signal processing, particularly for enhancing the signal-to-noise ratio in electroencephalograph (EEG) analysis. Novel methods based on deep learning demonstrate a notably prominent effect compared to traditional denoising approaches. However, those still suffer from certain limitations. Some methods often neglect the multi-domain characteristics of the artifact signal. Even among those that do consider these, there are deficiencies in terms of efficiency, effectiveness and computation cost.\n                    <jats:italic>Approach.<\/jats:italic>\n                    In this study, we propose a multiscale temporal convolution and spatial-spectral attention network with adversarial training for automatically filtering artifacts, named filter artifacts network (FLANet). The multiscale convolution module can extract sufficient temporal information and the spatial-spectral attention network can extract not only non-local similarity but also spectral dependencies. To make data denoising more efficient and accurate, we adopt adversarial training with novel loss functions to generate outputs that are closer to pure signals.\n                    <jats:italic>Main results.<\/jats:italic>\n                    The results show that the method proposed in this paper achieves great performance in artifact removal and valid information preservation on EEG signals contaminated by different types of artifacts. This approach enables a more optimal trade-off between denoising efficacy and computational overhead.\n                    <jats:italic>Significance.<\/jats:italic>\n                    The proposed artifact removal framework facilitates the implementation of an efficient denoising method, contributing to the advancement of neural analysis and neural engineering, and can be expected to be applied to clinical research and to realize novel human-computer interaction systems.\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/adae34","type":"journal-article","created":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T07:27:12Z","timestamp":1738654032000},"page":"016021","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["FLANet: A multiscale temporal convolution and spatial-spectral attention network for EEG artifact removal with adversarial training"],"prefix":"10.1088","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2767-0429","authenticated-orcid":true,"given":"Junkongshuai","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangjie","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0467-4347","authenticated-orcid":true,"given":"Lihua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongxue","family":"Gan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4372-9531","authenticated-orcid":true,"given":"Xiaoyang","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,2,4]]},"reference":[{"key":"jneadae34bib1","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.neucom.2016.10.024","article-title":"Brain computer interface: control signals review","volume":"223","author":"Ramadan","year":"2017","journal-title":"Neurocomputing"},{"key":"jneadae34bib2","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1162\/NECO_a_00838","article-title":"Electroencephalographic motor imagery brain connectivity analysis for BCI: a review","volume":"28","author":"Hamedi","year":"2016","journal-title":"Neural Comput."},{"key":"jneadae34bib3","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. Explor."},{"key":"jneadae34bib4","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.neucom.2012.05.009","article-title":"Artifact suppression from EEG signals using data adaptive time domain filtering","volume":"97","author":"Molla Md","year":"2012","journal-title":"Neurocomputing"},{"key":"jneadae34bib5","first-page":"501","article-title":"A new perspective of noise removal from eeg","author":"Li","year":"2017"},{"key":"jneadae34bib6","doi-asserted-by":"publisher","first-page":"4913","DOI":"10.1109\/JBHI.2021.3131104","article-title":"EEGANet: removal of ocular artifacts from the EEG signal using generative adversarial networks","volume":"26","author":"Sawangjai","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"jneadae34bib7","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"},{"key":"jneadae34bib8","first-page":"1110","article-title":"Removal of ocular artifacts from EEG: a comparison of adaptive filtering method and regression method using simulated data","author":"He","year":"2005"},{"key":"jneadae34bib9","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1109\/JBHI.2015.2450196","article-title":"The removal of EOG artifacts from EEG signals using independent component analysis and multivariate empirical mode decomposition","volume":"20","author":"Wang","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"jneadae34bib10","doi-asserted-by":"publisher","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":"jneadae34bib11","doi-asserted-by":"publisher","first-page":"1283","DOI":"10.1109\/JBHI.2022.3227320","article-title":"EEG reconstruction with a dual-scale CNN-LSTM model for deep artifact removal","volume":"27","author":"Gao","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"jneadae34bib12","doi-asserted-by":"publisher","first-page":"3607","DOI":"10.1109\/JBHI.2021.3131186","article-title":"Automated CCA-MWF algorithm for unsupervised identification and removal of EOG artifacts from EEG","volume":"26","author":"Miao","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"jneadae34bib13","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1109\/TNSRE.2016.2606416","article-title":"Minimum noise estimate filter: a novel automated artifacts removal method for field potentials","volume":"25","author":"Foodeh","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"jneadae34bib14","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac2bf8","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":"jneadae34bib15","first-page":"345","article-title":"Artifact removal in EEG using Morphological Component Analysis","author":"Yong","year":"2009"},{"key":"jneadae34bib16","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.neucom.2020.04.029","article-title":"A novel end-to-end 1D-ResCNN model to remove artifact from EEG signals","volume":"404","author":"Sun","year":"2020","journal-title":"Neurocomputing"},{"key":"jneadae34bib17","article-title":"Attention is all you need","volume":"vol 30","author":"Vaswani","year":"2017"},{"key":"jneadae34bib18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106248","article-title":"EEGDnet: fusing non-local and local self-similarity for EEG signal denoising with transformer","volume":"151","author":"Pu","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"jneadae34bib19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JBHI.2023.3277596","article-title":"A GAN guided parallel CNN and transformer network for EEG denoising","author":"Yin","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"jneadae34bib20","doi-asserted-by":"publisher","first-page":"1368","DOI":"10.1609\/aaai.v37i1.25221","article-title":"Spatial-spectral transformer for hyperspectral image denoising","volume":"vol 37","author":"Li","year":"2023"},{"key":"jneadae34bib21","first-page":"14214","article-title":"GALIP: generative adversarial clips for text-to-image synthesis","author":"Tao","year":"2023"},{"key":"jneadae34bib22","first-page":"1774","article-title":"Gdpp: learning diverse generations using determinantal point processes","author":"Elfeki","year":"2019"},{"key":"jneadae34bib23","first-page":"1","article-title":"Using determinant point process in generative adversarial networks for SSVEP signals synthesis","author":"Wang","year":"2023"},{"key":"jneadae34bib24","doi-asserted-by":"publisher","first-page":"2018","DOI":"10.1109\/TNSRE.2023.3266810","article-title":"Dual-encoder VAE-GAN with spatiotemporal features for emotional EEG data augmentation","volume":"31","author":"Tian","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"jneadae34bib25","doi-asserted-by":"publisher","DOI":"10.3389\/fnrgo.2021.805573","article-title":"Denoising EEG signals for real-world BCI applications using GANs","volume":"2","author":"Brophy","year":"2022","journal-title":"Front. Neuroergonomics"},{"key":"jneadae34bib26","doi-asserted-by":"publisher","first-page":"3524","DOI":"10.1109\/TNSRE.2023.3309815","article-title":"An approach for EEG denoising based on wasserstein generative adversarial network","volume":"31","author":"Dong","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"jneadae34bib27","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"jneadae34bib28","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"key":"jneadae34bib29","doi-asserted-by":"publisher","first-page":"9773","DOI":"10.1109\/TNNLS.2023.3236635","article-title":"Learning from local-global-graph representations for brain\u2013computer interface","volume":"35","author":"Ding","year":"2024","journal-title":"IEEE Trans. Neural Net. Learn. Syst."},{"key":"jneadae34bib30","first-page":"338","article-title":"MTSAN-MI: multiscale temporal-spatial convolutional self-attention network for motor imagery classification","volume":"vol 1963","author":"Wang","year":"2024"},{"key":"jneadae34bib31","first-page":"22","article-title":"A review on machine learning algorithms in handling EEG artifacts","volume":"14","author":"Barua","year":"2014"},{"key":"jneadae34bib32","first-page":"21","article-title":"An improved 3D human pose estimation model based on temporal convolution with Gaussian error linear units","author":"Kang","year":"2022"},{"key":"jneadae34bib33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3427471","article-title":"A flexible multichannel EEG artifact identification processor using depthwise-separable convolutional neural networks","volume":"17","author":"Khatwani","year":"2021","journal-title":"J. Emerg. Technol. Comput. Syst."},{"key":"jneadae34bib34","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abe397","article-title":"A new automated multi-stage system of non-local means and multi-kernel adaptive filtering techniques for EEG noise and artifacts suppression","volume":"18","author":"Eltrass","year":"2021","journal-title":"J. Neural Eng."},{"key":"jneadae34bib35","first-page":"1","article-title":"Investigation of EEG noise and artifact removal by patch-based and kernel adaptive filtering techniques","author":"Ghanem","year":"2018"},{"key":"jneadae34bib36","doi-asserted-by":"publisher","first-page":"579","DOI":"10.3390\/bioengineering10050579","article-title":"MultiResUNet3+: a full-scale connected multi-residual unet model to denoise electrooculogram and electromyogram artifacts from corrupted electroencephalogram signals","volume":"10","author":"Hossain","year":"2023","journal-title":"Bioengineering"},{"key":"jneadae34bib37","doi-asserted-by":"publisher","first-page":"0460c4","DOI":"10.1088\/1741-2552\/ac13c0","article-title":"Decoding imagined speech from EEG signals using hybrid-scale spatial-temporal dilated convolution network","volume":"18","author":"Li","year":"2021","journal-title":"J. Neural Eng."},{"key":"jneadae34bib38","article-title":"Mind the Pad\u2013CNNs can develop blind spots","author":"Alsallakh","year":"2020"},{"key":"jneadae34bib39","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.neucom.2022.10.054","article-title":"Classification of long sequential data using circular dilated convolutional neural networks","volume":"518","author":"Cheng","year":"2023","journal-title":"Neurocomputing"},{"key":"jneadae34bib40","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1561\/2200000044","article-title":"Determinantal point processes for machine learning","volume":"5","author":"Kulesza","year":"2012","journal-title":"Found. Trends\u00ae Mach. Learn."},{"key":"jneadae34bib41","doi-asserted-by":"publisher","first-page":"2947","DOI":"10.1109\/TPAMI.2018.2872043","article-title":"On the effectiveness of least squares generative adversarial networks","volume":"41","author":"Mao","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"jneadae34bib42","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/51.932724","article-title":"The impact of the MIT-BIH arrhythmia database","volume":"20","author":"Moody","year":"2001","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"jneadae34bib43","doi-asserted-by":"publisher","first-page":"55","DOI":"10.3389\/fnins.2012.00055","article-title":"Review of the BCI competition IV","volume":"6","author":"Tangermann","year":"2012","journal-title":"Front. Neurosci."},{"key":"jneadae34bib44","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1109\/TAU.1967.1161901","article-title":"The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms","volume":"15","author":"Welch","year":"1967","journal-title":"IEEE Trans. Audio Electroacoust."},{"key":"jneadae34bib45","first-page":"p 6","article-title":"A method for stochastic optimization","volume":"5","author":"Kinga","year":"2015"},{"key":"jneadae34bib46","first-page":"1","article-title":"SGDR: Stochastic Gradient Descent with Warm Restarts","author":"Loshchilov","year":"2017"},{"key":"jneadae34bib47","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.3390\/app10051605","article-title":"A novel simplified convolutional neural network classification algorithm of motor imagery EEG signals based on deep learning","volume":"10","author":"Li","year":"2020","journal-title":"Appl. Sci."},{"key":"jneadae34bib48","doi-asserted-by":"publisher","first-page":"14681","DOI":"10.1007\/s00521-021-06352-5","article-title":"Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review","volume":"35","author":"Altaheri","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"jneadae34bib49","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103698","article-title":"Weight-sharing network structure based on multi-channel EEG time-frequency map","volume":"76","author":"Liu","year":"2022","journal-title":"Biomed. Signal Process. Control"}],"container-title":["Journal of Neural Engineering"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/adae34","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/adae34\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/adae34\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/adae34\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T07:27:18Z","timestamp":1738654038000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/adae34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,1]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,2,4]]},"published-print":{"date-parts":[[2025,2,1]]}},"URL":"https:\/\/doi.org\/10.1088\/1741-2552\/adae34","relation":{},"ISSN":["1741-2560","1741-2552"],"issn-type":[{"value":"1741-2560","type":"print"},{"value":"1741-2552","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,1]]},"assertion":[{"value":"FLANet: A multiscale temporal convolution and spatial-spectral attention network for EEG artifact removal with adversarial training","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 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2024-01-24","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-01-24","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-02-04","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}