Ul Hassan, I., Ali, R. H., Abideen, Z. U., Ijaz, A. Z. & Khan, T. A. Towards effective emotion detection: A comprehensive machine learning approach on EEG signals. BioMedInformatics 3(4), 1083–1100 (2023).
Suhaimi, N. S., Mountstephens, J. & Teo, J. EEG-based emotion recognition: A state-of-the-art review of current trends and opportunities. Comput. Intell. Neurosci. 2020 (2020).
Doma, V. & Pirouz, M. A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals. J. Big Data 7(1), 1–21 (2020).
Google Scholar
Liu, Y., & Sourina, O. Real-time subject-dependent EEG-based emotion recognition algorithm. In Transactions on Computational Science XXIII: Special Issue on Cyberworlds 199–223 (2014).
Alhagry, S., Fahmy, A. A. & El-Khoribi, R. A. Emotion recognition based on EEG using LSTM recurrent neural network. Int. J. Adv. Comput. Sci. Appl. 8, 355–358 (2017).
Mohsen, S., Zekry, A. & Elshazly, A. Development of a portable DAQ based electroencephalogram system. Int. J. Comput. Appl. 975, 8887 (2016).
Google Scholar
Saxena, A., Khanna, A. & Gupta, D. Emotion recognition and detection methods: A comprehensive survey. J. Artif. Intell. Syst. 2(1), 53–79 (2020).
Google Scholar
Aldayel, M. & Kharrat, A. Predicting choices driven by emotional stimuli using EEG-based analysis and deep learning. Appl. Sci. 13(14), 8469 (2023).
Google Scholar
Mohsen, S. & Alharbi, A. G. EEG-based human emotion prediction using an LSTM model. In 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) 458–461 (IEEE, 2021).
Yoo, G., Kim, H. & Hong, S. Prediction of cognitive load from electroencephalography signals using long short-term memory network. Bioengineering 10(3), 361 (2023).
Soleymani, M., Pantic, M. & Pun, T. Multimodal emotion recognition in response to videos. IEEE Trans. Affect. Comput. 3(2), 211–223 (2011).
Wu, D., Zhang, J. & Zhao, Q. Multimodal fused emotion recognition about expression-EEG interaction and collaboration using deep learning. IEEE Access 8, 133180–133189 (2020).
Google Scholar
Egger, M., Ley, M. & Hanke, S. Emotion recognition from physiological signal analysis: A review. Electron. Notes Theor. Comput. Sci. 343, 35–55 (2019).
Google Scholar
Baig, M. & Kavakli, M. A survey on psycho-physiological analysis & measurement methods in multimodal systems. Multimodal Technol. Interact. 3(2), 37 (2019).
Google Scholar
Garg, D., Verma, G. K. & Singh, A. K. EEG-based emotion recognition using MobileNet recurrent neural network with time-frequency features. Appl. Soft Comput. 111338 (2024).
Miyamoto, K., Tanaka, H. & Nakamura, S. Online EEG-based emotion prediction and music generation for inducing affective states. IEICE. Trans. Inf. Syst. 105(5), 1050–1063 (2022).
Google Scholar
Sartipi, S., Torkamani-Azar, M. & Cetin, M. EEG emotion recognition via graph-based spatio-temporal attention neural networks. In 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 571–574 (IEEE, 2021).
Zhang, J., Chen, P., Nichele, S. & Yazidi, A. Emotion recognition using time-frequency analysis of EEG signals and machine learning. In 2019 IEEE Symposium Series on Computational Intelligence (SSCI) 404–409 (IEEE, 2019).
Wang, X., Ren, Y., Luo, Z., He, W., Huang, Y. & Jun Hong, and Deep learning-based EEG emotion recognition: Current trends and future perspectives. Front. Psychol. 14, 1126994 (2023).
Google Scholar
Lu, W., Liu, H., Ma, H., Tan, T. P. & Xia, L. Hybrid transfer learning strategy for cross-subject EEG emotion recognition. Front. Hum. Neurosci. 17 (2023).
Sebe, N., Cohen, I. & Huang, T. S. Multimodal emotion recognition. In Handbook of Pattern Recognition and Computer Vision 387–409 (2005).
Chen, J., Jiang, D. & Zhang, Y. A common spatial pattern and wavelet packet decomposition combined method for EEG-based emotion recognition. J. Adv. Comput. Intell. Intell. Inf. 23(2), 274–281 (2019).
Google Scholar
Huang, X., Oviatt, S. & Lunsford, R. Combining user modeling and machine learning to predict users’ multimodal integration patterns. In International Workshop on Machine Learning for Multimodal Interaction 50–62 (Springer, 2006).
Li, X. et al. A deep bidirectional long short-term memory based multi-scale approach for music dynamic emotion prediction. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 544–548 (IEEE, 2016).
Huang, D., Guan, C., Ang, K. K., Zhang, H. & Pan, Y. Asymmetric spatial pattern for EEG-based emotion detection. In The 2012 International Joint Conference on Neural Networks (IJCNN) 1–7 (IEEE, 2012).
Teo, J. & Jia Tian, C. Deep neural classifiers for EEG-based emotion recognition in immersive environments. In International Conference on Smart Computing and Electronic Enterprise (icscee) 1–6 (IEEE, 2018).
Wang, X. W., Nie, D. & Lu, B. L. EEG-based emotion recognition using frequency domain features and support vector machines. In Neural Information Processing: 18th International Conference, ICONIP 2011, Shanghai, China, November 13–17, 2011, Proceedings, Part I 18 734–743 (Springer, 2011).
Lin, W., Li, C. & Sun, S. Deep convolutional neural network for emotion recognition using EEG and peripheral physiological signal. In Image and Graphics: 9th International Conference, ICIG 2017, Shanghai, China, September 13–15, 2017, Revised Selected Papers, Part II 9 385–394 (Springer International Publishing, 2017).
Kim, S. H., Yang, H. J., Nguyen, N. A. T., Prabhakar, S. K. & Seong-Whan, L. Wedea: A new eeg-based framework for emotion recognition. IEEE J. Biomed. Health Inf. 26(1), 264–275 (2021).
Google Scholar
Chen, J., Zhu, Z. & Tony Ro, and Emotion recognition with audio, video, eeg, and emg: A dataset and baseline approaches. IEEE Access 10, 13229–13242 (2022).
Google Scholar
Diaz-Romero, D. J. et al. Recognizing emotional states with wearables while playing a serious game. IEEE Trans. Instrum. Meas. 70, 1–12 (2021).
Google Scholar
Peivandi, M., Ardabili, S. Z., Sheykhivand, S. & Danishvar, S. Deep learning for detecting multi-level driver fatigue using physiological signals: a comprehensive approach. Sensors 23(19), 8171 (2023).
Google Scholar
Sarma, P. & Barma, S. Review on stimuli presentation for affect analysis based on EEG. IEEE Access 8, 51991–52009 (2020).
Google Scholar
Alshahrani, H. M., Yaseen, I. & Drar, S. Emotion analysis using Improved Cat Swarm optimization with machine learning for Speech-impaired people. JDR 3(3). (2024).
Ahire, V. & Borse, S. Emotion detection from social media using machine learning techniques: A survey. In Applied Information Processing Systems 83–92 (Springer, 2022).
Aoki, T., Chujo, R., Matsui, K., Choi, S. & Hautasaari, A. EmoBalloon-conveying emotional arousal in text chats with speech balloons. In Proceedings of the CHI Conference on Human Factors in Computing Systems 1–16 (2022).
Catania, F. & Garzotto, F. A conversational agent for emotion expression stimulation in persons with neurodevelopmental disorders. Multimed. Tools Appl. 82(9), 12797–12828 (2023).
Google Scholar
Cui, B. et al. Emotion-based reinforcement attention network for depression detection on social media: algorithm development and validation. JMIR Med. Inf. 10(8), e37818 (2022).
Google Scholar
De, A. & Mishra, S. Augmented intelligence in mental health care: Sentiment analysis and emotion detection with health care perspective. In Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis 205–235 (Springer, 2022).
Kumar, S., Prabha, R. & Samuel, S. Sentiment analysis and emotion detection with healthcare perspective. In Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis 189–204 (Springer, 2022).
Google Scholar
EEG-human-emotion-dataset-2021, in the kaggle repository. Available: https://www.kaggle.com/drsaeedmohsen/eeghumanemotiondataset2021\.
Seyyedabbasi, A. Sand cat swarm optimization: A nature-inspired algorithm to solve global optimization problems. Eng. Comput. 39(4), 2627–2651 (2023).
Google Scholar
Wu, D. et al. and. Modified sand cat swarm optimization algorithm for solving constrained engineering optimization problems. Mathematics 10(22), 4350 (2022).
Seyyedabbasi, A. Binary sand cat swarm optimization algorithm for wrapper feature selection on biological data. Biomimetics 8(3), 310 (2023).
Li, Y. & Wang, G. Sand cat swarm optimization based on stochastic variation with elite collaboration. IEEE Access 10, 89989–90003 (2022).
Google Scholar
Hu, Y., Xiong, R., Li, J., Zhou, C. & Wu, Q.. An improved sand cat swarm operation and its application in engineering. IEEE Access (2023).
Kiani, F., Nematzadeh, S., Anka, F. A. & Findikli, M. A. Chaotic sand cat swarm optimization. Mathematics 11(10), 2340 (2023).
Google Scholar
Stankovic, M. et al. Feature selection and extreme learning machine tuning by hybrid sand cat optimization algorithm for diabetes classification. In International Conference on Modelling and Development of Intelligent Systems 188–203 (Springer, 2022).
Qtaish, A., Albashish, D., Braik, M., Alshammari, M. T., Alreshidi, A. & Alreshidi, E. J. Memory-based sand cat swarm optimization for feature selection in medical diagnosis. Electronics 12(9), 2042 (2023).
Yao, L., Yang, J., Yuan, P., Li, G., Lu, Y. & Zhang, T. Multi-strategy improved sand cat swarm optimization: Global optimization and feature selection. Biomimetics 8(6), 492 (2023).
Pashaei, E. An efficient binary sand cat swarm optimization for feature selection in high-dimensional biomedical data. Bioengineering 10(10), 1123 (2023).
link