{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T17:40:11Z","timestamp":1792086011624,"version":"4.4.0"},"reference-count":44,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:00:00Z","timestamp":1761523200000},"content-version":"vor","delay-in-days":26,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFF1204200"],"award-info":[{"award-number":["2023YFF1204200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62476197"],"award-info":[{"award-number":["62476197"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CNS Neurosci Ther"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Electroencephalogram (EEG) signals are crucial for brain\u2013computer interface research but are highly susceptible to noise contamination, necessitating effective denoising. While deep learning has been widely applied, its \u201cblack\u2010box\u201d nature limits interpretability. In contrast, traditional model\u2010based methods like Low\u2010Rank Recovery (LRR) offer strong interpretability by decomposing signals into low\u2010rank and sparse components.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This paper aims to develop an interpretable deep\u2010learning model for EEG denoising that combines the performance of deep learning with the interpretability of traditional LRR methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We propose LRR\u2010Unet, a deep unfolding network that transforms the traditional iterative LRR algorithm into a neural network architecture. Specifically, the time\u2010consuming Singular Value Decomposition (SVD) and sparse optimization processes in LRR are replaced with learnable neural network modules.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Extensive experiments demonstrate that LRR\u2010Unet outperforms other state\u2010of\u2010the\u2010art models in removing ocular and electromyographic artifacts, achieving superior performance on both quantitative and qualitative metrics. Furthermore, in downstream classification tasks, EEG signals preprocessed with LRR\u2010Unet yield better results across various evaluation indicators.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed LRR\u2010Unet provides an effective and interpretable solution for EEG denoising. Its superiority in denoising performance and practical utility in enhancing downstream application performance is validated through comprehensive experiments.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/cns.70632","type":"journal-article","created":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T12:56:47Z","timestamp":1761656207000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["<scp>LRR<\/scp>\n                    \u2010\n                    <scp>UNet<\/scp>\n                    : A Deep Unfolding Network With Low\u2010Rank Recovery for\n                    <scp>EEG<\/scp>\n                    Signal Denoising"],"prefix":"10.1111","volume":"31","author":[{"given":"Xiaoxiong","family":"Yue","sequence":"first","affiliation":[{"name":"Academy of Medical Engineering, and Translational Medicine Tianjin University  Tianjin 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