{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T17:55:57Z","timestamp":1791222957775,"version":"4.1.0"},"reference-count":49,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T00:00:00Z","timestamp":1791158400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Emotion recognition from EEG signals has become a central task in affective computing, offering non-invasive insight into human emotional states. Due to the nonlinear and nonstationary nature of EEG, entropy-based features have gained prominence as reliable indicators of brain complexity. However, traditional entropy metrics\u2014such as Shannon entropy (ShanEn) and its variants\u2014often lack sensitivity to transient dynamics and multiscale irregularities in the brain\u2019s electrical activity. This study introduces a novel application of Entropy of Entropy (EoE) to EEG phase series (EoE \u2013 phase), aiming to enhance the detection of emotional states by capturing second-order variability in signal complexity. Unlike conventional measures, EoE quantifies not just signal unpredictability, but also the fluctuation of that unpredictability over time, offering a more dynamic representation of neural information flow. Using the GAMEEMO dataset, which includes EEG recordings from 28 participants during four emotion-evoking game scenarios (funny, boring, horror, calm), we computed and compared four feature sets: Phase Entropy, EoE \u2013 phase, ShanEn, and EoE applied to EEG amplitude time series. These features were classified using six machine learning models, including AdaBoost, SVM, and Random Forest. Our results demonstrate that EoE applied to phase dynamics significantly outperforms other entropy-based features, achieving a classification accuracy of 71.7% and AUC of 0.869 using the RF classifier with mixed PhaseEn and EoE-phase features, while traditional Shannon-based features reached only ~59% accuracy. This study provides the first systematic evaluation of EoE on EEG phase signals for emotion recognition, revealing its strong discriminative power and potential for integration into next-generation brain-computer interfaces.<\/jats:p>","DOI":"10.1371\/journal.pone.0329598","type":"journal-article","created":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T17:29:57Z","timestamp":1791221397000},"page":"e0329598","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["EEG emotion recognition via multilevel entropy analysis: The power of entropy of entropy on phase dynamics"],"prefix":"10.1371","volume":"21","author":[{"given":"Morteza","family":"Jafari Malali","sequence":"first","affiliation":[{"name":"Shahid Beheshti University","place":["Tehran, Iran"],"department":["Faculty of Electrical Engineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sepideh","family":"Zolfaghari","sequence":"additional","affiliation":[{"name":"University of Tabriz","place":["Tabriz, Iran"],"department":["Faculty of Electrical and Computer Engineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3184-0582","authenticated-orcid":true,"given":"Raheleh","family":"Davoodi","sequence":"additional","affiliation":[{"name":"Shahid Beheshti University","place":["Tehran, Iran"],"department":["Faculty of Electrical Engineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,10,5]]},"reference":[{"issue":"1","key":"pone.0329598.ref001","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","article-title":"DEAP: A database for emotion analysis; using physiological signals","volume":"3","author":"S Koelstra","year":"2012","journal-title":"IEEE Trans Affective Comput"},{"issue":"6","key":"pone.0329598.ref002","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0246913","article-title":"EEG in game user analysis: A framework for expertise classification during gameplay","volume":"16","author":"T Hafeez","year":"2021","journal-title":"PLoS One"},{"issue":"15","key":"pone.0329598.ref003","doi-asserted-by":"crossref","first-page":"12527","DOI":"10.1007\/s00521-022-07292-4","article-title":"Human emotion recognition from EEG-based brain\u2013computer interface using machine learning: A comprehensive review","volume":"34","author":"EH Houssein","year":"2022","journal-title":"Neural Comput & Applic"},{"issue":"11","key":"pone.0329598.ref004","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0242014","article-title":"Tunable Q wavelet transform based emotion classification in Parkinson\u2019s disease using Electroencephalography","volume":"15","author":"M Murugappan","year":"2020","journal-title":"PLoS One"},{"issue":"10","key":"pone.0329598.ref005","doi-asserted-by":"crossref","first-page":"2508","DOI":"10.3390\/diagnostics12102508","article-title":"Use of differential entropy for automated emotion recognition in a virtual reality environment with EEG signals","volume":"12","author":"H Uyan\u0131k","year":"2022","journal-title":"Diagnostics (Basel)"},{"issue":"10","key":"pone.0329598.ref006","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0141281","article-title":"Multi-variate EEG analysis as a novel tool to examine brain responses to naturalistic music stimuli","volume":"10","author":"I Sturm","year":"2015","journal-title":"PLoS One"},{"key":"pone.0329598.ref007","first-page":"1","article-title":"Toward stochastic multiscale methods in continuum solid mechanics.","volume-title":"Advances in Applied Mechanics","author":"L. 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