Application of supervised machine learning models in human emotion classification using Tsallis entropy as a feature
Abstract Emotion identification acts as a critical component in passive brain-computer interfaces. The domain of EEG-based emotion identification has garnered substantial attention owing to advancements in machine learning models, notably in terms of higher accuracy and broader generalization capabi...
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SpringerOpen
2025-05-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-025-01177-8 |
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| author | Pragati Patel Sivarenjani B. Ramesh Naidu Annavarapu |
| author_facet | Pragati Patel Sivarenjani B. Ramesh Naidu Annavarapu |
| author_sort | Pragati Patel |
| collection | DOAJ |
| description | Abstract Emotion identification acts as a critical component in passive brain-computer interfaces. The domain of EEG-based emotion identification has garnered substantial attention owing to advancements in machine learning models, notably in terms of higher accuracy and broader generalization capabilities. Current research investigates the utility of Tsallis entropy as an emotion recognition feature from EEG signals. For this study, different EEG rhythms are extracted via FIR filter and are normalized. Tsallis entropy is then computed using a sliding 4-s time window with a 2-s overlap, forming a feature vector for testing five machine learning models: K Nearest Neighbor (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), and Decision Trees (DT). Additionally, the ensemble models KNN-DT and DT-LDA are also analyzed. The SEED dataset is employed for this study, and performance is evaluated through holdout cross-validation, considering accuracy, F1 score, precision, and recall metrices. Findings indicate that the DT-LDA ensemble model outperforms individual machine learning models and KNN-DT, achieving a significant accuracy increase of 5.6% to 11.2% for gamma rhythm. DT-LDA model displays the highest average classification accuracies 87.57% in gamma rhythm. This underscores the importance of high-frequency signals in emotion recognition, with gamma rhythm yielding the highest accuracy, while beta and all rhythms showing comparable results. These findings substantiate the efficacy of this methodology in the field of emotion identification utilizing EEG signals. |
| format | Article |
| id | doaj-art-27df0ed7461c443b835602c232e164c8 |
| institution | OA Journals |
| issn | 2196-1115 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SpringerOpen |
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| series | Journal of Big Data |
| spelling | doaj-art-27df0ed7461c443b835602c232e164c82025-08-20T01:53:19ZengSpringerOpenJournal of Big Data2196-11152025-05-0112111810.1186/s40537-025-01177-8Application of supervised machine learning models in human emotion classification using Tsallis entropy as a featurePragati Patel0Sivarenjani B.1Ramesh Naidu Annavarapu2Department of Physics, Pondicherry UniversityDepartment of Physics, Pondicherry UniversityDepartment of Physics, Pondicherry UniversityAbstract Emotion identification acts as a critical component in passive brain-computer interfaces. The domain of EEG-based emotion identification has garnered substantial attention owing to advancements in machine learning models, notably in terms of higher accuracy and broader generalization capabilities. Current research investigates the utility of Tsallis entropy as an emotion recognition feature from EEG signals. For this study, different EEG rhythms are extracted via FIR filter and are normalized. Tsallis entropy is then computed using a sliding 4-s time window with a 2-s overlap, forming a feature vector for testing five machine learning models: K Nearest Neighbor (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), and Decision Trees (DT). Additionally, the ensemble models KNN-DT and DT-LDA are also analyzed. The SEED dataset is employed for this study, and performance is evaluated through holdout cross-validation, considering accuracy, F1 score, precision, and recall metrices. Findings indicate that the DT-LDA ensemble model outperforms individual machine learning models and KNN-DT, achieving a significant accuracy increase of 5.6% to 11.2% for gamma rhythm. DT-LDA model displays the highest average classification accuracies 87.57% in gamma rhythm. This underscores the importance of high-frequency signals in emotion recognition, with gamma rhythm yielding the highest accuracy, while beta and all rhythms showing comparable results. These findings substantiate the efficacy of this methodology in the field of emotion identification utilizing EEG signals.https://doi.org/10.1186/s40537-025-01177-8Machine learning modelsEmotion recognitionTsallis entropyNonextensive statisticsEnsemble learning model |
| spellingShingle | Pragati Patel Sivarenjani B. Ramesh Naidu Annavarapu Application of supervised machine learning models in human emotion classification using Tsallis entropy as a feature Journal of Big Data Machine learning models Emotion recognition Tsallis entropy Nonextensive statistics Ensemble learning model |
| title | Application of supervised machine learning models in human emotion classification using Tsallis entropy as a feature |
| title_full | Application of supervised machine learning models in human emotion classification using Tsallis entropy as a feature |
| title_fullStr | Application of supervised machine learning models in human emotion classification using Tsallis entropy as a feature |
| title_full_unstemmed | Application of supervised machine learning models in human emotion classification using Tsallis entropy as a feature |
| title_short | Application of supervised machine learning models in human emotion classification using Tsallis entropy as a feature |
| title_sort | application of supervised machine learning models in human emotion classification using tsallis entropy as a feature |
| topic | Machine learning models Emotion recognition Tsallis entropy Nonextensive statistics Ensemble learning model |
| url | https://doi.org/10.1186/s40537-025-01177-8 |
| work_keys_str_mv | AT pragatipatel applicationofsupervisedmachinelearningmodelsinhumanemotionclassificationusingtsallisentropyasafeature AT sivarenjanib applicationofsupervisedmachinelearningmodelsinhumanemotionclassificationusingtsallisentropyasafeature AT rameshnaiduannavarapu applicationofsupervisedmachinelearningmodelsinhumanemotionclassificationusingtsallisentropyasafeature |