{
  "abstract": "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—such as Shannon entropy (ShanEn) and its variants—often lack sensitivity to transient dynamics and multiscale irregularities in the brain’s electrical activity. This study introduces a novel application of Entropy of Entropy (EoE) to EEG phase series (EoE – 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 – 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.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran",
    "Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Morteza Jafari Malali"
    },
    {
      "affs": [
        1
      ],
      "name": "Sepideh Zolfaghari"
    },
    {
      "name": "Raheleh Davoodi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9ejeae53d98cf.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0329598",
  "fulltext_url": null,
  "id": "9ejeae53d98cf",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0329598",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3205|Emotion and Mood Recognition"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Brain mapping/Electroencephalography"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognition"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Neuroimaging/Electroencephalography"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Neurophysiology/Brain electrophysiology/Electroencephalography"
    ],
    [
      "plos",
      "/Biology and life sciences/Physiology/Electrophysiology/Neurophysiology/Brain electrophysiology/Electroencephalography"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Emotions"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Information theory/Information entropy"
    ],
    [
      "plos",
      "/Computer and information sciences/Systems science/Dynamical systems"
    ],
    [
      "plos",
      "/Computer and information sciences/Systems science/Nonlinear dynamics"
    ],
    [
      "plos",
      "/Medicine and health sciences/Clinical medicine/Clinical neurophysiology/Electroencephalography"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Systems science/Dynamical systems"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Systems science/Nonlinear dynamics"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Thermodynamics/Entropy"
    ],
    [
      "plos",
      "/Research and analysis methods/Bioassays and physiological analysis/Electrophysiological techniques/Brain electrophysiology/Electroencephalography"
    ],
    [
      "plos",
      "/Research and analysis methods/Imaging techniques/Neuroimaging/Electroencephalography"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Emotions"
    ]
  ],
  "openalex": "W7220368901",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0329598&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/9ejeae53d98cf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:37Z",
      "native_id": "10.1371/journal.pone.0329598",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0329598"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    },
    {
      "field": "mathematics",
      "origin": "plos:dynamical systems",
      "subfield": "dynamical-systems"
    }
  ],
  "title": "EEG emotion recognition via multilevel entropy analysis: The power of entropy of entropy on phase dynamics",
  "title_text": "EEG emotion recognition via multilevel entropy analysis: The power of entropy of entropy on phase dynamics",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:37Z",
  "url": "https://research.draconitic.com/paper/9ejeae53d98cf",
  "venue": "PLOS One",
  "versions": []
}