Analysis of auditory attention based on different semantic levels using a Multi-objective Coati optimization algorithm

Analysis of auditory attention based on different semantic levels using a Multi-objective Coati optimization algorithm | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of auditory attention based on different semantic levels using a Multi-objective Coati optimization algorithm Sushma S Jagtap, T. Manikandan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7920688/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Auditory attention is a cognitive mechanism which permits us to consciously pay attention to certain sounds while neglecting others. It is captured by various sources such as electroencephalogram (EEG), heart rate, photoplethysmogram (PPG), and galvanic skin response (GSR) signals. Out of them, the EEG signal is employed for evaluating auditory attention due to it is a non-invasive and complex signal that has several applications in biomedical domains. In this study we used Physiology of Auditory attention (PhyAAt). It consists of a dataset of 25 healthy volunteers performing three distinct tasks (resting, writing, and listening) under various noise levels, including native speakers and non-native speakers. To our knowledge, there is no attempt at different semantic level classification using optimization-based channel selection, especially for this dataset. So, in this work we examine auditory attention at various semantic levels. To increase the efficiency of classification while minimizing computational complexity, we utilize the multi objective Coati Optimization Algorithm (MOCOA) for channel selection and voting classifiers. From the findings, it suggests that semantic-level classification performed better, whether channel selection was used or not. Auditory attention COA EEG PhyAAt Voting Classifiers Full Text Additional Declarations No competing interests reported. Figures 1, 2, 3, 5 and 6 are not available with this version. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 19 May, 2026 Reviewers agreed at journal 18 May, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 11 Nov, 2025 Submission checks completed at journal 11 Nov, 2025 First submitted to journal 22 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7920688","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626208539,"identity":"75a4e541-2790-41d9-86cd-ffdda676dbd3","order_by":0,"name":"Sushma S Jagtap","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABU0lEQVRIie2SP0vDQBiH3/DCubR2TTkwXyGlUC1F/SAuCRlcUlG6dCjhQuC6FLumUPQjaJfODYFOha4nFTEWOnURoSAWMX+KQxvBUTAPx/He8Xu433AAGRl/ExJtmMxNAIkhkxiM4ns5Xj8qkTWJFOl3SmJJPBo2CkB6XnHPFy+iZe0Vunfe29XNk4Jtz351149n+4DeLAdHF1uKKszDsjn2URZzpL1ho2R3dKc44Is6B2LUciA3thXZJNQkIwThA80PNclmOisGzA+VXIWGis52i1Hz00JF+PiR72undjdw3p/XkVJYpSkgtAqtc0R16hCaZ5puuzov3pP4FZKmqJNlqFz7WBJYqbljzbDdgFd7PFSQlKt9dbdYOyq2soyDqTefXba040HX8B86YbHbthOIZdPaKbbBAFlLvkDpOxEf1fR8yAkURklG+TGTkZGR8V/5Aty3d92m2UbIAAAAAElFTkSuQmCC","orcid":"","institution":"Rajalakshmi Engineering College","correspondingAuthor":true,"prefix":"","firstName":"Sushma","middleName":"S","lastName":"Jagtap","suffix":""},{"id":626208540,"identity":"7ec5e20d-42cf-4c64-823e-93dd6e9af186","order_by":1,"name":"T. 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