A Transformer Network-driven Deep Learning Architecture for Compressed Sensing

A Transformer Network-driven Deep Learning Architecture for Compressed Sensing | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 6 September 2025 V1 Latest version Share on A Transformer Network-driven Deep Learning Architecture for Compressed Sensing Authors : Zhiying Xu 0009-0004-0516-1046 [email protected] and Huotao Gao Authors Info & Affiliations https://doi.org/10.22541/au.175714782.28599277/v1 182 views 111 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This study proposes Transformer CS-EEG, a novel Transformer-based deep learning framework designed for compressed sensing of EEG signals. By leveraging the self-attention mechanism, our approach effectively captures long-range dependencies and intricate spatio-temporal correlations inherent in EEG data.Comprehensive experiments on three public EEG datasets validate that our approach consistently surpasses state-of-the-art methods across diverse compression ratios, yielding up to 18% reduction in reconstruction error and 2.4 dB improvement in signal-to-noise ratio. Additionally, the method preserves critical neurophysiological information, maintaining over 96% of the original accuracy in downstream EEG analysis tasks. Supplementary Material File (a transformer network-driven deep learning architecture for compressed sensing.docx) Download 324.50 KB Information & Authors Information Version history V1 Version 1 06 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords compressed sensing electroencephalogram signals network-driven deep learning architecture transformer cs-eeg Authors Affiliations Zhiying Xu 0009-0004-0516-1046 [email protected] Wuhan University View all articles by this author Huotao Gao Wuhan University View all articles by this author Metrics & Citations Metrics Article Usage 182 views 111 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Zhiying Xu, Huotao Gao. A Transformer Network-driven Deep Learning Architecture for Compressed Sensing. Authorea . 06 September 2025. DOI: https://doi.org/10.22541/au.175714782.28599277/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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