Optimized Emotion Recognition Using Swarm Intelligence and Explainable AI for EEG Based Analysis

Optimized Emotion Recognition Using Swarm Intelligence and Explainable AI for EEG Based Analysis | 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 Optimized Emotion Recognition Using Swarm Intelligence and Explainable AI for EEG Based Analysis Niharika Gudikandula, Ravichander Janapati, Sridhar Chintala This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7451229/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 The identification of the emotional arousal and valence states of the people has been a major concern in various disciplines, includingthe adaptation of social life to differently able people. Automated identification systems for such purposes can not rely solely onbasic postures and facial gestures alone. Advanced methodologies, such as neural signal analysis, can complement conventionalmethodologies by using EEG data with a view to understanding emotional states and cognitive processes. In order to develop arobust system, in the current work, we utilized the GAMEEMO dataset, which has the advantage of combining the flexibility ofincorporating various game environment variables and coupling them with corresponding psychological state labels. For the purposeof this study, two emotions are considered for the classification: Positive and Negative emotions. Acquiring EEG signals from thebrain underwent some pre-processing techniques such as bandpass filter and wavelet transform is sensitive to temporal frequencyvariations. Subsequently, feature extraction is performed using the Fast Fourier Transform from which vectors were extracted.Finally, the extracted time-frequency features were applied to a machine learning technique called Support Vector Machines, havinga particle swam optimization classifier for emotion classification. Our study considers multiple emotions, specifically ’Arousal’ and’Valence’ for the precision of classification of LANV, LAPV, HANV and HAPV. These experimental results represent the completework, proving the significance and efficiency of the proposed SVM with the PSO achieved 99.37% of the accuracy of emotiondetection. In addition, our study used explainable artificial intelligence methods like SHAP and LIME to determine which featuresare the most important to improve the accuracy of EEG-based emotion classifiers. Electroencephalography GAMEEMO dataset Particle Swarm Optimization Support Vector Machine Explainable artificial intelligence Full Text Additional Declarations No competing interests reported. Supplementary Files SVMPSO3.zip 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-7451229","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534859449,"identity":"7dadce7b-1292-4a0b-a7e8-069f273c4caa","order_by":0,"name":"Niharika 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