Review of EEG Based Taste Classification Methods

Review of EEG Based Taste Classification Methods | 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 Review of EEG Based Taste Classification Methods Priaansh Gupta, Barsha V, Gannshree R, Prashant P. Patavardhan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8339414/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This review covers the new field of using technology like EEG (brain-wave reading) and BCI (Brain Computer Interface) to objectively classify taste, offering a more reliable alternative to traditional methods. It analyzes studies from 2015 to 2024 detailing the steps signal acquisition preprocessing feature extraction and classification algorithms. A key finding from 2015 is the high accuracy (over 98%) in distinguishing sweet and sour tastes using specific features. The review also highlights recent advances like deep learning and channel optimization that maintain high accuracy while reducing computational power. It examines the pros and cons of different research designs including electrode placement and classification results. Ultimately the article discusses the potential of this technology as a reliable objective tool for the food industry quality control clinical applications and advanced assistive technologies. By far the most pressing problems, namely, inter, individual variability and the limited number of taste categories studied so far, lead to very precise directions for future work. Among these are the imperative to broaden research to cover a wider spectrum of tastes, to develop portable EEG systems and to improve cross subject generalization capabilities. This paper is a comprehensive guide for students and researchers alike, helping them to comprehend the complex functioning of such systems, and their potential impact on society. Brain Computer Interface Electroencephalography Taste Classification Feature Extraction Signal Processing Machine Learning Wavelet Entropy Linear Discriminant Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-8339414","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":559754460,"identity":"307579bb-06e1-4028-a53e-56f4ad87caac","order_by":0,"name":"Priaansh Gupta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYFACxgYQmQDhVAAxM3MD0VqArDMgLYyEtEAARAtjG8IQnMBc+nDjh5876vLkI3KPP/g5rzaavx2o5UfFNpxaLPsSmyV7zxwuNryRl9jYu+147ozDQNt6ztzGqcXgDGODBG/bgcSNM3IMG3i3HcttAGphZmzDq6X559+2OrCWxr9zjuXOJ0JLmzRvG3PifIkcw2behprcDYS0WPYwtlnLth1O3MDzLnG2zLEDuRuBWg7i84s5D/vjm2+BDpvfnnvg45uautx55w8ffPCjAo/D4IwDPCDqMJhzAKd6ZC3yDWAtdfgUj4JRMApGwQgFAO0YY2YXHG/IAAAAAElFTkSuQmCC","orcid":"","institution":"RV Institute of Technology and Management","correspondingAuthor":true,"prefix":"","firstName":"Priaansh","middleName":"","lastName":"Gupta","suffix":""},{"id":559754462,"identity":"24e9aceb-6dc7-414d-bf59-0b9a26fb6523","order_by":1,"name":"Barsha V","email":"","orcid":"","institution":"RV Institute of Technology and Management","correspondingAuthor":false,"prefix":"","firstName":"Barsha","middleName":"","lastName":"V","suffix":""},{"id":559754465,"identity":"e846a273-1480-44a7-87cf-bd3ce53eb23d","order_by":2,"name":"Gannshree R","email":"","orcid":"","institution":"RV Institute of Technology and Management","correspondingAuthor":false,"prefix":"","firstName":"Gannshree","middleName":"","lastName":"R","suffix":""},{"id":559754466,"identity":"e1c2602b-f2db-4b29-b8ff-a5fc900d2bd7","order_by":3,"name":"Prashant P. 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