A comparative analysis of emotion recognition from EEG signals using temporal features and hyperparameter-tuned machine learning techniques
Classifying emotions based on EEG signals is really important for enhancing our interactions with computers, monitoring mental health and creating applications in affective computing field. This study explores improving emotion recognition performance by applying traditional machine learning classif...
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| Vydáno v: | MethodsX Ročník 15; s. 103468 |
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01.12.2025
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| Abstract | Classifying emotions based on EEG signals is really important for enhancing our interactions with computers, monitoring mental health and creating applications in affective computing field. This study explores improving emotion recognition performance by applying traditional machine learning classifiers and boosting techniques to EEG data from the DEAP dataset. To categorize emotional states, we used four classifiers: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), XGBoost and Gradient Boosting. Differential entropy and Higuchi's fractal dimension are two important time-domain parameters that we extracted after applying a segmentation technique to capture the temporal interdependence of EEG data. These features were selected for their ability to reflect intricate neural dynamics associated with emotional processing. A five-fold cross-validation procedure was applied to estimate the model's performance and hyperparameter tuning was conducted to optimize classifier efficiency. XGBoost achieved the highest accuracy 89 % for valence and 88 % for arousal demonstrating its superior performance. Furthermore, cross-subject evaluation on the SEED dataset reinforced the approach’s robustness, where XGBoost achieved 86 % accuracy using HFD and 84 % using DE. These results emphasize the effectiveness of combining advanced feature extraction methods with boosting algorithms for EEG-based emotion recognition, offering promising directions for the development of real-world emotion-aware systems. The key findings of this research are as follows:•Differential Entropy and Higuchi’s Fractal Dimension proved effective in capturing emotional brain dynamics•XGBoost outperformed other classifiers in both DEAP and SEED datasets•The proposed method demonstrates robustness across subject variations and datasets
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| AbstractList | Classifying emotions based on EEG signals is really important for enhancing our interactions with computers, monitoring mental health and creating applications in affective computing field. This study explores improving emotion recognition performance by applying traditional machine learning classifiers and boosting techniques to EEG data from the DEAP dataset. To categorize emotional states, we used four classifiers: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), XGBoost and Gradient Boosting. Differential entropy and Higuchi's fractal dimension are two important time-domain parameters that we extracted after applying a segmentation technique to capture the temporal interdependence of EEG data. These features were selected for their ability to reflect intricate neural dynamics associated with emotional processing. A five-fold cross-validation procedure was applied to estimate the model's performance and hyperparameter tuning was conducted to optimize classifier efficiency. XGBoost achieved the highest accuracy 89 % for valence and 88 % for arousal demonstrating its superior performance. Furthermore, cross-subject evaluation on the SEED dataset reinforced the approach's robustness, where XGBoost achieved 86 % accuracy using HFD and 84 % using DE. These results emphasize the effectiveness of combining advanced feature extraction methods with boosting algorithms for EEG-based emotion recognition, offering promising directions for the development of real-world emotion-aware systems. The key findings of this research are as follows:•Differential Entropy and Higuchi's Fractal Dimension proved effective in capturing emotional brain dynamics•XGBoost outperformed other classifiers in both DEAP and SEED datasets•The proposed method demonstrates robustness across subject variations and datasets.Classifying emotions based on EEG signals is really important for enhancing our interactions with computers, monitoring mental health and creating applications in affective computing field. This study explores improving emotion recognition performance by applying traditional machine learning classifiers and boosting techniques to EEG data from the DEAP dataset. To categorize emotional states, we used four classifiers: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), XGBoost and Gradient Boosting. Differential entropy and Higuchi's fractal dimension are two important time-domain parameters that we extracted after applying a segmentation technique to capture the temporal interdependence of EEG data. These features were selected for their ability to reflect intricate neural dynamics associated with emotional processing. A five-fold cross-validation procedure was applied to estimate the model's performance and hyperparameter tuning was conducted to optimize classifier efficiency. XGBoost achieved the highest accuracy 89 % for valence and 88 % for arousal demonstrating its superior performance. Furthermore, cross-subject evaluation on the SEED dataset reinforced the approach's robustness, where XGBoost achieved 86 % accuracy using HFD and 84 % using DE. These results emphasize the effectiveness of combining advanced feature extraction methods with boosting algorithms for EEG-based emotion recognition, offering promising directions for the development of real-world emotion-aware systems. The key findings of this research are as follows:•Differential Entropy and Higuchi's Fractal Dimension proved effective in capturing emotional brain dynamics•XGBoost outperformed other classifiers in both DEAP and SEED datasets•The proposed method demonstrates robustness across subject variations and datasets. Classifying emotions based on EEG signals is really important for enhancing our interactions with computers, monitoring mental health and creating applications in affective computing field. This study explores improving emotion recognition performance by applying traditional machine learning classifiers and boosting techniques to EEG data from the DEAP dataset. To categorize emotional states, we used four classifiers: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), XGBoost and Gradient Boosting. Differential entropy and Higuchi's fractal dimension are two important time-domain parameters that we extracted after applying a segmentation technique to capture the temporal interdependence of EEG data. These features were selected for their ability to reflect intricate neural dynamics associated with emotional processing. A five-fold cross-validation procedure was applied to estimate the model's performance and hyperparameter tuning was conducted to optimize classifier efficiency. XGBoost achieved the highest accuracy 89 % for valence and 88 % for arousal demonstrating its superior performance. Furthermore, cross-subject evaluation on the SEED dataset reinforced the approach's robustness, where XGBoost achieved 86 % accuracy using HFD and 84 % using DE. These results emphasize the effectiveness of combining advanced feature extraction methods with boosting algorithms for EEG-based emotion recognition, offering promising directions for the development of real-world emotion-aware systems. The key findings of this research are as follows:•Differential Entropy and Higuchi's Fractal Dimension proved effective in capturing emotional brain dynamics•XGBoost outperformed other classifiers in both DEAP and SEED datasets•The proposed method demonstrates robustness across subject variations and datasets. Classifying emotions based on EEG signals is really important for enhancing our interactions with computers, monitoring mental health and creating applications in affective computing field. This study explores improving emotion recognition performance by applying traditional machine learning classifiers and boosting techniques to EEG data from the DEAP dataset. To categorize emotional states, we used four classifiers: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), XGBoost and Gradient Boosting. Differential entropy and Higuchi's fractal dimension are two important time-domain parameters that we extracted after applying a segmentation technique to capture the temporal interdependence of EEG data. These features were selected for their ability to reflect intricate neural dynamics associated with emotional processing. A five-fold cross-validation procedure was applied to estimate the model's performance and hyperparameter tuning was conducted to optimize classifier efficiency. XGBoost achieved the highest accuracy 89 % for valence and 88 % for arousal demonstrating its superior performance. Furthermore, cross-subject evaluation on the SEED dataset reinforced the approach’s robustness, where XGBoost achieved 86 % accuracy using HFD and 84 % using DE. These results emphasize the effectiveness of combining advanced feature extraction methods with boosting algorithms for EEG-based emotion recognition, offering promising directions for the development of real-world emotion-aware systems. The key findings of this research are as follows:•Differential Entropy and Higuchi’s Fractal Dimension proved effective in capturing emotional brain dynamics•XGBoost outperformed other classifiers in both DEAP and SEED datasets•The proposed method demonstrates robustness across subject variations and datasets [Display omitted] Classifying emotions based on EEG signals is really important for enhancing our interactions with computers, monitoring mental health and creating applications in affective computing field. This study explores improving emotion recognition performance by applying traditional machine learning classifiers and boosting techniques to EEG data from the DEAP dataset. To categorize emotional states, we used four classifiers: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), XGBoost and Gradient Boosting. Differential entropy and Higuchi's fractal dimension are two important time-domain parameters that we extracted after applying a segmentation technique to capture the temporal interdependence of EEG data. These features were selected for their ability to reflect intricate neural dynamics associated with emotional processing. A five-fold cross-validation procedure was applied to estimate the model's performance and hyperparameter tuning was conducted to optimize classifier efficiency. XGBoost achieved the highest accuracy 89 % for valence and 88 % for arousal demonstrating its superior performance. Furthermore, cross-subject evaluation on the SEED dataset reinforced the approach’s robustness, where XGBoost achieved 86 % accuracy using HFD and 84 % using DE. These results emphasize the effectiveness of combining advanced feature extraction methods with boosting algorithms for EEG-based emotion recognition, offering promising directions for the development of real-world emotion-aware systems. The key findings of this research are as follows: • Differential Entropy and Higuchi’s Fractal Dimension proved effective in capturing emotional brain dynamics • XGBoost outperformed other classifiers in both DEAP and SEED datasets • The proposed method demonstrates robustness across subject variations and datasets |
| ArticleNumber | 103468 |
| Author | Hasan, Rabita Islam, Sheikh Md. Rabiul |
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| Keywords | Feature extraction EEG signals Boosted EEG Emotion Classification Using Differential Entropy and Higuchi's Fractal Dimension Emotion classification Machine learning DEAP dataset |
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| References | Mehreen, Anwar, Haseeb, Majid, Ullah (bib0009) 2019; 20 Piana, Stagliano, Odone, Camurri (bib0003) 2016; 6 Nawaz, Cheah, Nisar, Yap (bib0029) 2020; 40 Rahman, Sarkar, Hossain, Moni (bib0011) 2022; 207 Koelstra (bib0026) 2012; 3 Gannouni, Aledaily, Belwafi, Aboalsamh (bib0007) 2022; 319 Nawaz, Nisar, Voon (bib0019) Jul. 2018; 6 Qayyum, Majid, Haq, Anwar (bib0008) 2019 Bhatti, Majid, Anwar, Khan (bib0024) 2016; 65 Chen, Ju, Yuan, Elhoseny, Ren, Fan, Chen (bib0023) 2018; 72 Alhalaseh, Alasasfeh (bib0017) Nov. 2020; 9 Alves, Melo, Cerri, de Freitas, Adamatti, de Aguiar (bib0033) 2024; 37 Khateeb, Anwar, Alnowami (bib0025) 2021; 9 Hasan, Islam, Khan (bib0012) 2024 Valderas, Bolea, Laguna, Bailon, Vallverdu (bib0005) 2019 Kajal, Fioravanti, Elshahabi, Ruiz, Sitaram, Braun (bib0006) 2020; 222 Hancer, Subasi (bib0016) 2023 Miranda, Canabal, Garcia, Lopez-Ongil (bib0004) 2018; 2208 Yuvaraj, Thagavel, Thomas, Fogarty, Ali (bib0028) 2023; 23 Fang, Yang, Zhang, Liu, Tao (bib0020) 2020 Menezes (bib0014) 2017; 21 Yang, Wu, Fu, Chen (bib0027) 2018; 11307 Zhuang, Zeng, Tong, Zhang, Zhang, Yan (bib0034) Aug. 2017; 2017 Gonzalez, Yoo, Elfadel (bib0022) July 2019 Krstajic, Buturovic, Leahy, Thomas (bib0031) 2014; 6 Alreshidi, Ullah (bib0002) 2020; 7 Veeramallu (bib0015) 2019 Mohamed, Jusas (bib0010) 2024; 14 (bib0032) 2021 Liu, Meng, Li, Zhang, Qin, Nandi (bib0030) 2018; 30 Vanitha, krishnan (bib0013) 2018; 23 Zheng, Lu (bib0035) Sep. 2015; 7 Zheng, Zhu, Lu (bib0021) 2019; 10 Zhang, Huang, Gao (bib0001) 2017; 20 Z. Mohammadi et al., “Wavelet based emotion recognition structure using EEG signal”, Springer, pp. 1987–1993, 2017. Khateeb (10.1016/j.mex.2025.103468_bib0025) 2021; 9 Zhuang (10.1016/j.mex.2025.103468_bib0034) 2017; 2017 Fang (10.1016/j.mex.2025.103468_bib0020) 2020 10.1016/j.mex.2025.103468_bib0018 Zheng (10.1016/j.mex.2025.103468_bib0035) 2015; 7 Rahman (10.1016/j.mex.2025.103468_bib0011) 2022; 207 Alves (10.1016/j.mex.2025.103468_bib0033) 2024; 37 Valderas (10.1016/j.mex.2025.103468_bib0005) 2019 Chen (10.1016/j.mex.2025.103468_bib0023) 2018; 72 Krstajic (10.1016/j.mex.2025.103468_bib0031) 2014; 6 Alhalaseh (10.1016/j.mex.2025.103468_bib0017) 2020; 9 Gannouni (10.1016/j.mex.2025.103468_bib0007) 2022; 319 Vanitha (10.1016/j.mex.2025.103468_bib0013) 2018; 23 Bhatti (10.1016/j.mex.2025.103468_bib0024) 2016; 65 Hasan (10.1016/j.mex.2025.103468_bib0012) 2024 Veeramallu (10.1016/j.mex.2025.103468_bib0015) 2019 Mohamed (10.1016/j.mex.2025.103468_bib0010) 2024; 14 Piana (10.1016/j.mex.2025.103468_bib0003) 2016; 6 Koelstra (10.1016/j.mex.2025.103468_bib0026) 2012; 3 Zhang (10.1016/j.mex.2025.103468_bib0001) 2017; 20 Mehreen (10.1016/j.mex.2025.103468_bib0009) 2019; 20 (10.1016/j.mex.2025.103468_bib0032) 2021 Gonzalez (10.1016/j.mex.2025.103468_bib0022) 2019 Yang (10.1016/j.mex.2025.103468_bib0027) 2018; 11307 Menezes (10.1016/j.mex.2025.103468_bib0014) 2017; 21 Nawaz (10.1016/j.mex.2025.103468_bib0029) 2020; 40 Hancer (10.1016/j.mex.2025.103468_bib0016) 2023 Zheng (10.1016/j.mex.2025.103468_bib0021) 2019; 10 Kajal (10.1016/j.mex.2025.103468_bib0006) 2020; 222 Alreshidi (10.1016/j.mex.2025.103468_bib0002) 2020; 7 Nawaz (10.1016/j.mex.2025.103468_bib0019) 2018; 6 Liu (10.1016/j.mex.2025.103468_bib0030) 2018; 30 Miranda (10.1016/j.mex.2025.103468_bib0004) 2018; 2208 Qayyum (10.1016/j.mex.2025.103468_bib0008) 2019 Yuvaraj (10.1016/j.mex.2025.103468_bib0028) 2023; 23 |
| References_xml | – volume: 6 start-page: 5 year: 2014 end-page: 20 ident: bib0031 article-title: Cross-validation pitfalls when selecting and assessing regression and classification models publication-title: J. Cheminform. – volume: 222 year: 2020 ident: bib0006 publication-title: Involvement of Top-Down Networks in the Perception of Facial emotions: A magnetoencephalographic Investigation – year: July 2019 ident: bib0022 article-title: EEG-based emotion detection using unsupervised transfer learning publication-title: Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) – start-page: 3 year: 2019 end-page: 9 ident: bib0015 article-title: EEG based automatic emotion recognition using EMD and RF classifier publication-title: 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) – volume: 7 start-page: 1 year: 2020 end-page: 16 ident: bib0002 article-title: Facial emotion recognition using hybrid features publication-title: Informatics – volume: 11307 start-page: 436 year: 2018 end-page: 446 ident: bib0027 article-title: Continuous convolutional neural network with 3D input for EEG-based emotion recognition publication-title: Springer – volume: 37 year: 2024 ident: bib0033 article-title: A machine learning pipeline for emotion recognition based on brain topographic maps derived from electroencephalogram signals publication-title: 2024 International FLAIRS Conference Proceedings – year: 2019 ident: bib0005 article-title: Mutual information between heart rate variability and respiration for emotion characterization publication-title: Physiol. Meas – volume: 20 start-page: 5121 year: 2019 end-page: 5128 ident: bib0009 article-title: A hybrid scheme for drowsiness detection using wearable sensors publication-title: IEEE Sens. J – volume: 6 start-page: 45195 year: Jul. 2018 end-page: 45211 ident: bib0019 article-title: The effect of music on Human brain: frequency domain and time series analysis using electroencephalogram publication-title: IEEe Access. – reference: Z. Mohammadi et al., “Wavelet based emotion recognition structure using EEG signal”, Springer, pp. 1987–1993, 2017. – volume: 21 start-page: 1007 year: 2017 end-page: 1018 ident: bib0014 article-title: Towards emotion recognition for virtual environments: an evaluation of EEG features on bench-mark dataset publication-title: Pers. Ubiquitous Comput. – year: 2019 ident: bib0008 article-title: Generation of personalized video summaries by detecting viewer’s emotion using eeg publication-title: J. Vis. Commun. Image Represent – volume: 72 start-page: 385 year: 2018 end-page: 395 ident: bib0023 article-title: Emotion recognition using empirical mode decomposition and approximation entropy publication-title: Comput. Electr. Eng. – year: 2021 ident: bib0032 article-title: What is the benefit of k-fold cross-validation? publication-title: Cross Validated Stack Exch. – volume: 20 start-page: 1574 year: 2017 end-page: 1592 ident: bib0001 article-title: Speech emotion recognition using deep CNN and discriminant temporal pyramid matching publication-title: IEEE Trans. Multimed – volume: 65 start-page: 270 year: 2016 end-page: 278 ident: bib0024 article-title: Human emotion recognition and analysis in response to audio music using brain signals publication-title: Comput. Hum. Behav. – volume: 30 start-page: 5 year: 2018 end-page: 18 ident: bib0030 article-title: Emotion detection from EEG recordings based on supervised and unsupervised dimension reduction publication-title: Concurr. Comput.: Pract. Exp. – volume: 207 start-page: 118028 year: 2022 end-page: 118040 ident: bib0011 article-title: EEG-based emotion analysis using non-linear features and ensemble learning approaches publication-title: Expert Syst. Appl. – volume: 14 start-page: 11323 year: 2024 end-page: 11335 ident: bib0010 article-title: Developing innovative feature extraction techniques from the emotion recognition field on motor imagery using brain–computer interface EEG signals publication-title: Appl. Sci. – volume: 6 start-page: 3 year: 2016 end-page: 33 ident: bib0003 article-title: Adaptive body gesture representation for automatic emotion recognition publication-title: ACM Trans. Interact. Intell. Syst. – volume: 9 start-page: 97 year: Nov. 2020 ident: bib0017 article-title: Machine-learning-based emotion recognition system using EEG signals publication-title: Computers – volume: 23 year: 2023 ident: bib0028 article-title: Comprehensive analysis of feature extraction methods for emotion recognition from multichannel EEG recordings publication-title: Sensors – volume: 7 start-page: 162 year: Sep. 2015 end-page: 175 ident: bib0035 article-title: ‘Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks,’’ publication-title: IEEE Trans. Aut. Ment. Dev. – volume: 319 start-page: 420 year: 2022 end-page: 430 ident: bib0007 article-title: EEG based emotion detection using ensemble classification and asymmetric brain activity publication-title: J. Affect. Disord. – volume: 3 start-page: 18 year: 2012 end-page: 31 ident: bib0026 article-title: DEAP: a database for emotion analysis; using physiological signals publication-title: IEEE Trans. Affect Comput. – volume: 2208 start-page: 25 year: 2018 end-page: 32 ident: bib0004 article-title: Embedded emotion recognition: autonomous multimodal affective IOTs publication-title: Proc. Cyber-Phys. Syst. Workshop – volume: 23 year: 2018 ident: bib0013 article-title: Time-frequency analysis of EEG for improved classification of emotion publication-title: Int. J. Biomed. Eng. Technol. – year: 2023 ident: bib0016 article-title: EEG-based emotion recognition using dual tree complex wavelet transform and random subspace ensemble classifier publication-title: Comput. Methods Biomech. Biomed. Eng.: Imaging Vis. – volume: 9 year: 2021 ident: bib0025 article-title: Multi-domain feature fusion for emotion classification using DEAP dataset publication-title: IEEe Access. – start-page: 1 year: 2024 end-page: 6 ident: bib0012 article-title: Machine Learning Techniques for Brain Stroke Analysis and Prediction publication-title: IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON) – volume: 40 start-page: 915 year: 2020 end-page: 930 ident: bib0029 article-title: Comparison of different feature extraction methods for EEG-based emotion recognition publication-title: Biocybern. Biomed. Eng. – volume: 10 start-page: 420 year: 2019 end-page: 432 ident: bib0021 article-title: Identifying stable patterns over time for emotion recognition from EEG publication-title: IEEe Trans. Affect Comput. – volume: 2017 start-page: 3 year: Aug. 2017 end-page: 12 ident: bib0034 article-title: ‘Emotion recognition from EEG signals using multidimensional information in EMD domain publication-title: BioMed. Res. Int. – year: 2020 ident: bib0020 article-title: Multi-feature input deep forest for EEG-based emotion recognition publication-title: Front. Neurorobot. – volume: 40 start-page: 915 year: 2020 ident: 10.1016/j.mex.2025.103468_bib0029 article-title: Comparison of different feature extraction methods for EEG-based emotion recognition publication-title: Biocybern. Biomed. Eng. doi: 10.1016/j.bbe.2020.04.005 – volume: 23 issue: 6–10 year: 2018 ident: 10.1016/j.mex.2025.103468_bib0013 article-title: Time-frequency analysis of EEG for improved classification of emotion publication-title: Int. J. Biomed. Eng. Technol. – volume: 2017 start-page: 3 year: 2017 ident: 10.1016/j.mex.2025.103468_bib0034 article-title: ‘Emotion recognition from EEG signals using multidimensional information in EMD domain publication-title: BioMed. Res. Int. doi: 10.1155/2017/8317357 – volume: 319 start-page: 420 year: 2022 ident: 10.1016/j.mex.2025.103468_bib0007 article-title: EEG based emotion detection using ensemble classification and asymmetric brain activity publication-title: J. Affect. Disord. doi: 10.1016/j.jad.2022.09.054 – volume: 7 start-page: 162 issue: 3 year: 2015 ident: 10.1016/j.mex.2025.103468_bib0035 article-title: ‘Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks,’’ publication-title: IEEE Trans. Aut. Ment. Dev. doi: 10.1109/TAMD.2015.2431497 – volume: 9 start-page: 97 issue: 4 year: 2020 ident: 10.1016/j.mex.2025.103468_bib0017 article-title: Machine-learning-based emotion recognition system using EEG signals publication-title: Computers doi: 10.3390/computers9040095 – start-page: 1 year: 2024 ident: 10.1016/j.mex.2025.103468_bib0012 article-title: Machine Learning Techniques for Brain Stroke Analysis and Prediction – volume: 10 start-page: 420 issue: 3 year: 2019 ident: 10.1016/j.mex.2025.103468_bib0021 article-title: Identifying stable patterns over time for emotion recognition from EEG publication-title: IEEe Trans. Affect Comput. doi: 10.1109/TAFFC.2017.2712143 – volume: 7 start-page: 1 issue: 1 year: 2020 ident: 10.1016/j.mex.2025.103468_bib0002 article-title: Facial emotion recognition using hybrid features publication-title: Informatics doi: 10.3390/informatics7010006 – volume: 6 start-page: 3 year: 2016 ident: 10.1016/j.mex.2025.103468_bib0003 article-title: Adaptive body gesture representation for automatic emotion recognition publication-title: ACM Trans. Interact. Intell. Syst. doi: 10.1145/2818740 – volume: 20 start-page: 5121 issue: 15 year: 2019 ident: 10.1016/j.mex.2025.103468_bib0009 article-title: A hybrid scheme for drowsiness detection using wearable sensors publication-title: IEEE Sens. J – year: 2019 ident: 10.1016/j.mex.2025.103468_bib0008 article-title: Generation of personalized video summaries by detecting viewer’s emotion using eeg publication-title: J. Vis. Commun. Image Represent doi: 10.1016/j.jvcir.2019.102672 – volume: 23 issue: 920 year: 2023 ident: 10.1016/j.mex.2025.103468_bib0028 article-title: Comprehensive analysis of feature extraction methods for emotion recognition from multichannel EEG recordings publication-title: Sensors – year: 2020 ident: 10.1016/j.mex.2025.103468_bib0020 article-title: Multi-feature input deep forest for EEG-based emotion recognition publication-title: Front. Neurorobot. – volume: 11307 start-page: 436 year: 2018 ident: 10.1016/j.mex.2025.103468_bib0027 article-title: Continuous convolutional neural network with 3D input for EEG-based emotion recognition publication-title: Springer – year: 2019 ident: 10.1016/j.mex.2025.103468_bib0022 article-title: EEG-based emotion detection using unsupervised transfer learning – volume: 2208 start-page: 25 year: 2018 ident: 10.1016/j.mex.2025.103468_bib0004 article-title: Embedded emotion recognition: autonomous multimodal affective IOTs publication-title: Proc. Cyber-Phys. Syst. Workshop – volume: 222 year: 2020 ident: 10.1016/j.mex.2025.103468_bib0006 – volume: 14 start-page: 11323 issue: 11 year: 2024 ident: 10.1016/j.mex.2025.103468_bib0010 article-title: Developing innovative feature extraction techniques from the emotion recognition field on motor imagery using brain–computer interface EEG signals publication-title: Appl. Sci. doi: 10.3390/app142311323 – volume: 3 start-page: 18 issue: 1 year: 2012 ident: 10.1016/j.mex.2025.103468_bib0026 article-title: DEAP: a database for emotion analysis; using physiological signals publication-title: IEEE Trans. Affect Comput. doi: 10.1109/T-AFFC.2011.15 – volume: 20 start-page: 1574 year: 2017 ident: 10.1016/j.mex.2025.103468_bib0001 article-title: Speech emotion recognition using deep CNN and discriminant temporal pyramid matching publication-title: IEEE Trans. Multimed – volume: 65 start-page: 270 year: 2016 ident: 10.1016/j.mex.2025.103468_bib0024 article-title: Human emotion recognition and analysis in response to audio music using brain signals publication-title: Comput. Hum. Behav. doi: 10.1016/j.chb.2016.08.029 – volume: 9 year: 2021 ident: 10.1016/j.mex.2025.103468_bib0025 article-title: Multi-domain feature fusion for emotion classification using DEAP dataset publication-title: IEEe Access. doi: 10.1109/ACCESS.2021.3051281 – year: 2021 ident: 10.1016/j.mex.2025.103468_bib0032 article-title: What is the benefit of k-fold cross-validation? publication-title: Cross Validated Stack Exch. – year: 2019 ident: 10.1016/j.mex.2025.103468_bib0005 article-title: Mutual information between heart rate variability and respiration for emotion characterization publication-title: Physiol. Meas doi: 10.1088/1361-6579/ab310a – volume: 207 start-page: 118028 year: 2022 ident: 10.1016/j.mex.2025.103468_bib0011 article-title: EEG-based emotion analysis using non-linear features and ensemble learning approaches publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.118025 – volume: 72 start-page: 385 year: 2018 ident: 10.1016/j.mex.2025.103468_bib0023 article-title: Emotion recognition using empirical mode decomposition and approximation entropy publication-title: Comput. Electr. Eng. doi: 10.1016/j.compeleceng.2018.09.022 – start-page: 3 year: 2019 ident: 10.1016/j.mex.2025.103468_bib0015 article-title: EEG based automatic emotion recognition using EMD and RF classifier – volume: 21 start-page: 1007 issue: 6 year: 2017 ident: 10.1016/j.mex.2025.103468_bib0014 article-title: Towards emotion recognition for virtual environments: an evaluation of EEG features on bench-mark dataset publication-title: Pers. Ubiquitous Comput. doi: 10.1007/s00779-017-1072-7 – volume: 6 start-page: 5 issue: 1 year: 2014 ident: 10.1016/j.mex.2025.103468_bib0031 article-title: Cross-validation pitfalls when selecting and assessing regression and classification models publication-title: J. Cheminform. doi: 10.1186/1758-2946-6-10 – year: 2023 ident: 10.1016/j.mex.2025.103468_bib0016 article-title: EEG-based emotion recognition using dual tree complex wavelet transform and random subspace ensemble classifier publication-title: Comput. Methods Biomech. Biomed. Eng.: Imaging Vis. doi: 10.1080/10255842.2022.2143714 – volume: 37 year: 2024 ident: 10.1016/j.mex.2025.103468_bib0033 article-title: A machine learning pipeline for emotion recognition based on brain topographic maps derived from electroencephalogram signals – ident: 10.1016/j.mex.2025.103468_bib0018 doi: 10.1007/s00521-015-2149-8 – volume: 30 start-page: 5 issue: 1 year: 2018 ident: 10.1016/j.mex.2025.103468_bib0030 article-title: Emotion detection from EEG recordings based on supervised and unsupervised dimension reduction publication-title: Concurr. Comput.: Pract. Exp. – volume: 6 start-page: 45195 year: 2018 ident: 10.1016/j.mex.2025.103468_bib0019 article-title: The effect of music on Human brain: frequency domain and time series analysis using electroencephalogram publication-title: IEEe Access. doi: 10.1109/ACCESS.2018.2855194 |
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