EmoDLNet: End-to-End Multi-Scale Spatio-Temporal Deep Learning for EEG-Based Emotion Recognition in Affective Human-Computer Interaction

Byl P de. A conceptual affective design framework for the use of emotions in computer game design. Cyberpsychology: Journal of Psychosocial Research on Cyberspace. 2015; 9(3). https://doi.org/10.5817/CP2015-3-4

Dozio N, Marcolin F, Scurati GW, Ulrich L, Nonis F, Vezzetti E, Marsocci G, La Rosa A, Ferrise F. A design methodology for affective virtual reality. Int J Hum Comput Stud. 2022;162:102791. https://doi.org/10.1016/j.ijhcs.2022.102791.

Article  Google Scholar 

Md FK, Khondakar MH, Sarowar MH, Chowdhury S, Majumder MA, Hossain MAA, Dewan QD, Hossain. A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques. Brain Inf. 2024;11:17. https://doi.org/10.1186/s40708-024-00229-8.

Article  Google Scholar 

Alazrai R, Homoud R, Alwanni H, Daoud M. EEG-based emotion recognition using quadratic time-frequency distribution. sensors. 2018;18:2739. https://doi.org/10.3390/s18082739.

Article  Google Scholar 

Ayata D, Yaslan Y, Kamasak M. Emotion recognition via random forest and galvanic skin response: Comparison of time based feature sets, window sizes and wavelet approaches, in: 2016 Medical Technologies National Congress (TIPTEKNO), IEEE, 2016: pp. 1–4. https://doi.org/10.1109/TIPTEKNO.2016.7863130

Bulagang AF, Weng NG, Mountstephens J, Teo J. A review of recent approaches for emotion classification using electrocardiography and electrodermography signals. Inf Med Unlocked. 2020;20:100363. https://doi.org/10.1016/j.imu.2020.100363.

Article  Google Scholar 

Lim JZ, Mountstephens J, Teo J. Emotion recognition using eye-tracking: taxonomy, review and current challenges. Sensors. 2020;20:2384. https://doi.org/10.3390/s20082384.

Article  Google Scholar 

Maithri M, Raghavendra U, Gudigar A, Samanth J, Barua PD, Murugappan M, Chakole Y, Acharya UR. Automated emotion recognition: current trends and future perspectives. Comput Methods Programs Biomed. 2022;215:106646. https://doi.org/10.1016/j.cmpb.2022.106646

Article  Google Scholar 

Rashid M, Sulaiman N, PP Abdul Majeed A, Musa RM, Ab NsirAF,, Bari BS, Khatun S. Current status, challenges, and possible solutions of EEG-based brain-computer interface: a comprehensive review. Frontiers in Neurorobotics. 2020;14 :515104. https://doi.org/10.3389/fnbot.2020.00025

Wang J, Wang M. Review of the emotional feature extraction and classification using EEG signals. Cogn Rob. 2021;1:29–40. https://doi.org/10.1016/j.cogr.2021.04.001.

Article  Google Scholar 

Zhao H, Zheng Q, Ma K, Li H, Zheng Y. Deep representation-based domain adaptation for nonstationary EEG classification. IEEE Trans Neural Networks Learn Syst. 2021;32:535–45. https://doi.org/10.1109/TNNLS.2020.3010780.

Article  Google Scholar 

Geng Y, Shi S, Hao X. Deep learning-based EEG emotion recognition: a comprehensive review. Neural Comput Applic. 2025;37:1919–50. https://doi.org/10.1007/s00521-024-10821-y.

Article  Google Scholar 

Li D, Xie L, Wang Z, Yang H. Brain emotion perception inspired EEG emotion recognition with deep reinforcement learning. IEEE Trans Neural Networks Learn Syst. 2024;35:12979–92. https://doi.org/10.1109/TNNLS.2023.3265730.

Article  Google Scholar 

Gong L, Li M, Zhang T, Chen W. EEG emotion recognition using attention-based convolutional transformer neural network. Biomed Signal Process Control. 2023;84:104835. https://doi.org/10.1016/j.bspc.2023.104835.

Article  Google Scholar 

Tao W, Li C, Song R, Cheng J, Liu Y, Wan F, Chen X. EEG-based emotion recognition via channel-wise attention and self Attention. IEEE Trans Affect Comput. 2023;14:382–93. https://doi.org/10.1109/TAFFC.2020.3025777.

Article  Google Scholar 

Guo W, Wang Y. Convolutional gated recurrent unit-driven multidimensional dynamic graph neural network for subject-independent emotion recognition. Expert Syst Appl. 2024;238:121889. https://doi.org/10.1016/j.eswa.2023.121889.

Article  Google Scholar 

Fan Z, Chen F, Xia X, Liu Y. EEG emotion classification based on graph convolutional network. Appl Sci. 2024;14:726. https://doi.org/10.3390/app14020726.

Article  Google Scholar 

Russell JA. Emotion, core affect, and psychological construction. Cognition Emot. 2009;23:1259–83. https://doi.org/10.1080/02699930902809375.

Article  Google Scholar 

Koelstra S, Muhl C, Soleymani M, Lee J-S, Yazdani A, Ebrahimi T, Pun T, Nijholt A, Patras I. DEAP: a database for emotion analysis;using physiological signals. IEEE Trans Affect Comput. 2012;3:18–31. https://doi.org/10.1109/T-AFFC.2011.15.

Article  Google Scholar 

Soleymani M, Pantic M, Pun T. Multimodal emotion recognition in response to videos. IEEE Trans Affect Comput. 2012;3:211–23. https://doi.org/10.1109/T-AFFC.2011.37.

Article  Google Scholar 

Song T, Zheng W, Song P, Cui Z. EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans Affect Comput. 2020;11:532–41. https://doi.org/10.1109/TAFFC.2018.2817622.

Article  Google Scholar 

Kumar A, Kumar A. BiLSTM-based human emotion classification using EEG signal. Clin EEG Neurosci. 2025; 57(3):1-9. https://doi.org/10.1177/15500594251364017

Miranda-Correa JA, Abadi MK, Sebe N, Patras I. A dataset for affect, personality and mood research on individuals and groups. IEEE Trans Affect Comput. 2021;12:479–93. https://doi.org/10.1109/TAFFC.2018.2884461.

Article  Google Scholar 

Bagherzadeh S, Norouzi MR, Bahri Hampa S, Ghasri A, Tolou Kouroshi P, Hosseininasab S, Ghasem Zadeh MA, Nasrabadi AM. A subject-independent portable emotion recognition system using synchrosqueezing wavelet transform maps of EEG signals and ResNet-18. Biomed Signal Process Control. 2024;90:105875. https://doi.org/10.1016/j.bspc.2023.105875.

Article  Google Scholar 

Mendivil Sauceda JA, Marquez BY, Esqueda Elizondo JJ. Emotion classification from electroencephalographic signals using machine learning. Brain Sci. 2024;14:1211. https://doi.org/10.3390/brainsci14121211.

Article  Google Scholar 

Katsigiannis S, Ramzan N. A database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices. IEEE J Biomed Health Inf. 2018;22:98–107. https://doi.org/10.1109/JBHI.2017.2688239.

Article  Google Scholar 

Topic A, Russo M. Emotion recognition based on EEG feature maps through deep learning network. Eng Sci Technol Int J. 2021;24:1442–54. https://doi.org/10.1016/j.jestch.2021.03.012.

Article  Google Scholar 

Liu Y, Ding Y, Li C, Cheng J, Song R, Wan F, Chen X. Multi-channel EEG-based emotion recognition via a multi-level features guided capsule network. Comput Biol Med. 2020;123:103927. https://doi.org/10.1016/j.compbiomed.2020.103927.

Article  Google Scholar 

Cui H, Liu A, Zhang X, Chen X, Wang K, Chen X. EEG-based emotion recognition using an end-to-end regional-asymmetric convolutional neural network. Knowl Based Syst. 2020;205:106243. https://doi.org/10.1016/j.knosys.2020.106243.

Article  Google Scholar 

Nawaz R, Cheah KH, Nisar H, Yap VV. Comparison of different feature extraction methods for EEG-based emotion recognition. Biocybernetics Biomedical Eng. 2020;40:910–26. https://doi.org/10.1016/j.bbe.2020.04.005.

Article  Google Scholar 

Li X, Song D, Zhang P, Zhang Y, Hou Y, Hu B. Exploring EEG features in cross-subject emotion recognition. Front NeuroSci. 2018;12:162.

Article  Google Scholar 

Zhong J, Tang YM, Ng KC, Yung KL. Intelligent Health Inspection for Road Multi-part Covers Based on Vibration Feature Encoding and Denoising Diffusion Model. IEEE Transactions on Instrumentation and Measurement . 2025;74: 1–11 . https://doi.org/10.1109/TIM.2025.3548795

Gao Q, Yang Y, Kang Q, Tian Z, Song Y. EEG-based emotion recognition with feature fusion networks. Int J Mach Learn Cybernet. 2022;13:421–9. https://doi.org/10.1007/s13042-021-01414-5.

Article  Google Scholar 

Şengür D, Siuly S. Efficient approach for EEG-based emotion recognition. Electron Lett. 2020;56:1361–4. https://doi.org/10.1049/el.2020.2685.

Article  Google Scholar 

Song T, Zheng W, Liu S, Zong Y, Cui Z, Li Y. Graph-embedded convolutional neural network for image-based EEG emotion recognition. IEEE Trans Emerg Top Comput. 2022;10:1399–413. https://doi.org/10.1109/TETC.2021.3087174.

Article  Google Scholar 

Zhang J, Yin Z, Chen P, Nichele S. Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review. Inform Fusion. 2020;59:103–26. https://doi.org/10.1016/j.inffus.2020.01.011.

Article  Google Scholar 

Guo W, Li Y, Liu M, Ma R, Wang Y. Functional connectivity-enhanced feature-grouped attention network for cross-subject EEG emotion recognition. Knowl Based Syst. 2024;283:111199. https://doi.org/10.1016/j.knosys.2023.111199.

Article  Google Scholar 

Chen L, Wen S, Tang Y, Ma Y, Wang H, Geda MW. Brain power mapping with optimized deep learning for EEG-based pilot fatigue detection. Biomed Signal Process Control. 2026;114:109284. https://doi.org/10.1016/j.bspc.2025.109284.

Article  Google Scholar 

Schirrmeister RT, Springenberg JT, Fiederer LDJ, Glasstetter M, Eggensperger K, Tangermann M, Hutter F, Burgard W, Ball T. Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp. 2017;38:5391–420. https://doi.org/10.1002/hbm.23730.

Article  Google Scholar 

Lawhern VJ, Solon AJ, Waytowich NR, Gordon SM, Hung CP, Lance BJ. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. J Neural Eng. 2018;15:056013. https://doi.org/10.1088/1741-2552/aace8c.

Article  Google Scholar 

Anuragi A, Singh Sisodia D, Bilas Pachori R. EEG-based cross-subject emotion recognition using Fourier-Bessel series expansion based empirical wavelet transform and NCA feature selection method. Inf Sci. 2022;610:508–24. https://doi.org/10.1016/j.ins.2022.07.121.

Article  Google Scholar 

Fan C, Wang J, Huang W, Yang X, Pei G, Li T, Lv Z. Light-weight residual convolution-based capsule network for EEG emotion recognition. Adv Eng Inform. 2024;61:102522. https://doi.org/10.1016/j.aei.2024.102522.

Article 

Comments (0)

No login
gif