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Cross subject emotion identification from multichannel EEG sub-bands using Tsallis entropy feature and KNN classifier

Brain informatics, 2024-12, Vol.11 (1), p.7-7 [Peer Reviewed Journal]

The Author(s) 2024 ;2024. The Author(s). ;COPYRIGHT 2024 Springer ;The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2198-4018 ;EISSN: 2198-4026 ;DOI: 10.1186/s40708-024-00220-3 ;PMID: 38441825

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  • Title:
    Cross subject emotion identification from multichannel EEG sub-bands using Tsallis entropy feature and KNN classifier
  • Author: Patel, Pragati ; Balasubramanian, Sivarenjani ; Annavarapu, Ramesh Naidu
  • Subjects: Artificial Intelligence ; Brain region ; Classifiers ; Cognitive Psychology ; Computation by Abstract Devices ; Computer Science ; Datasets ; EEG channel selection ; EEG signal ; Electroencephalography ; Emotion identification ; Emotion recognition ; Emotional factors ; Emotions ; Entropy ; Feature engineering ; Frequency ranges ; Health Informatics ; Human-computer interface ; Neurophysiology ; Neurosciences ; Parameters ; Performance enhancement ; Tsallis entropy
  • Is Part Of: Brain informatics, 2024-12, Vol.11 (1), p.7-7
  • Description: Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain–computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4–7 Hz), alpha-α (8–15 Hz), beta-β (16–31 Hz), gamma-γ (32–55 Hz), and the overall frequency range (0–75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F -score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q  = 3. In addition, the highest accuracy and F -score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements. Highlights Subject independent human emotion identification is studied using SEED data set. Tsallis entropy is employed as feature and performance variation with Tsallis parameter ( q  =  2, 3, 4) is examined. Performance of kNN classifier is examined with Tsallis entropy feature. Emotion identification at various levels is studied, brain region, EEG rhythms, brain hemisphere. Prospects of TsEn-based real-time emotion recognition framework is canvassed.
  • Publisher: Berlin/Heidelberg: Springer Berlin Heidelberg
  • Language: English
  • Identifier: ISSN: 2198-4018
    EISSN: 2198-4026
    DOI: 10.1186/s40708-024-00220-3
    PMID: 38441825
  • Source: ProQuest One Psychology
    PubMed Central
    Springer Nature OA/Free Journals
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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