EEG emotion recognition via multilevel entropy analysis: The power of entropy of entropy on phase dynamics
- 1Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
- 2Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
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.
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BibTeX
@article{malali2026emotion,
title = {{EEG emotion recognition via multilevel entropy analysis: The power of entropy of entropy on phase dynamics}},
author = {Morteza Jafari Malali and Sepideh Zolfaghari and Raheleh Davoodi},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0329598},
url = {https://doi.org/10.1371/journal.pone.0329598},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - EEG emotion recognition via multilevel entropy analysis: The power of entropy of entropy on phase dynamics
AU - Morteza Jafari Malali
AU - Sepideh Zolfaghari
AU - Raheleh Davoodi
PY - 2026
DA - 2026/10/05
JO - PLOS One
DO - 10.1371/journal.pone.0329598
UR - https://doi.org/10.1371/journal.pone.0329598
AB - 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.
SN - 1932-6203
ER -