Taran S, Bajaj V. Emotion recognition from single-channel EEG signals using a two-stage correlation and instantaneous frequency-based filtering method. Comput Meth Programs Biomed. 2019;173:157–65.
Damasio A. DESCARTES’ ERROR: Emotion, Reason, and the Human Brain, vol. 3. New York, NY, USA: McGraw-Hill; 1964.
Hazarika D, Zimmermann R, Poria S. Misa: Modality-invariant and-specific representations for multimodal sentiment analysis. In: Proceedings of the 28th ACM International Conference on Multimedia, 2020;pp. 1122–1131
Kang H, Hazarika D, Kim D, Kim J. Zero-shot visual emotion recognition by exploiting BERT. In: Proceedings of SAI Intelligent Systems Conference, 2022;pp. 485–494
Feng L, Cheng C, Zhao M, Deng H, Zhang Y. EEG-based emotion recognition using spatial-temporal graph convolutional LSTM with attention mechanism. IEEE J Biomed Health Inform. 2022;26(11):5406–17.
Zheng WL, Zhu JY, Lu BL. Identifying stable patterns over time for emotion recognition from EEG. IEEE Trans Affect Comput. 2019;10(3):417–29.
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(5): 056013.
Zheng WL, Liu W, Lu Y, Lu BL, Cichocki A. Emotionmeter: a multimodal framework for recognizing human emotions. IEEE Trans Cybern. 2019;49(3):1110–22.
Song T, Zheng W, Song P, Cui Z. EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans Affect Comput. 2020;11(3):532–41.
Song Y, Zheng Q, Liu B, Gao X. EEG conformer: convolutional transformer for EEG decoding and visualization. IEEE Trans Neural Syst Rehabil Eng. 2022;31:710–9.
Zhong P, Wang D, Miao C. EEG-based emotion recognition using regularized graph neural networks. IEEE Trans Affect Comput. 2022;13(3):1290–301.
Zheng WL, Lu BL. Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans Auton Ment Dev. 2015;7(3):162–75.
Wang XW, Nie D, Lu BL. Emotional state classification from EEG data using machine learning approach. Neurocomputing. 2014;129:94–106.
Du X, Ma C, Zhang G, Li J, Lai YK, Zhao G, Deng X, et al. An efficient LSTM network for emotion recognition from multichannel EEG signals. IEEE Trans Affect Comput. 2022;13(3):1528–40.
Alhagry S, Fahmy AA, El-Khoribi RA. Emotion recognition based on EEG using LSTM recurrent neural network. Int J Adv Comput Sci Appl, 2017;8(10)
Pascanu R, Gulcehre C, Cho K, Bengio Y. How to construct deep recurrent neural networks. In: ICLR 2014.
Glorot X, Bordes A, Bengio Y. Domain adaptation for large-scale sentiment classification: A deep learning approach. In: Proceedings of the 28th International Conference on Machine Learning, 2011;pp. 513–520
Goodfellow I, Lee H, Le Q, Saxe A, Ng A. Measuring invariances in deep networks. In: Advances in Neural Information Processing Systems, 2009;vol. 22
Sainath TN, Vinyals O, Senior A, Sak H. Convolutional, long short-term memory, fully connected deep neural networks. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing, 2015;pp. 4580–4584
Shi X, Chen Z, Wang H, Yeung DY, Wong WK, Woo WC. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, 2015;vol. 28
Li X, Song D, Zhang P, Yu G, Hou Y, Hu B. Emotion recognition from multi-channel EEG data through convolutional recurrent neural network. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine, 2016;pp. 352–359
Zhang D, Yao L, Zhang X, Wang S, Chen W, Boots R, Benatallah B. Cascade and parallel convolutional recurrent neural networks on EEG-based intention recognition for brain computer interface. In: Proceedings of the Aaai Conference on Artificial Intelligence, 2018;vol. 32
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, 2017;vol. 30
Dai Z, Yang Z, Yang Y, Carbonell J, Le Q, Salakhutdinov R. Transformer-XL: Attentive language models beyond a fixed-length context. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019;pp. 2978–2988
Andayani F, Theng LB, Tsun MT, Chua C. Hybrid LSTM-transformer model for emotion recognition from speech audio files. IEEE Access. 2022;10:36018–27.
Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018;pp. 7132–7141
Rudakov E, Laurent L, Cousin V, Roshdi A, Fournier R, Nait-Ali A, Beyrouthy T et al. Multi-Task CNN model for emotion recognition from EEG Brain maps. In: 4th International Conference on Bio-Engineering for Smart Technologies, 2021;pp. 1–4
Yang Y, Wu Q, Fu Y, Chen X. Continuous convolutional neural network with 3D input for EEG-based emotion recognition. In: Neural Information Processing: 25th International Conference, 2018;pp. 433–443
Yang Y, Wu Q, Qiu M, Wang Y, Chen X. Emotion recognition from multi-channel EEG through parallel convolutional recurrent neural network. In: 2018 International Joint Conference on Neural Networks, 2018;pp. 1–7
Maaten LV, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9(11):2579–605.
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, 2017;pp. 618–626
Li X, Zhang Y, Tiwari P, Song D, Hu B, Yang M, Zhao Z, Kumar N, Marttinen P. EEG based emotion recognition: a tutorial and review. ACM Comput Surv. 2022;55(4):1–57.
Park J, Woo S, Lee JY, Kweon IS. BAM: Bottleneck Attention Module. In: BMVC, 2018;pp. 1–14
Zhao Z, Liu Q, Zhou F. Robust lightweight facial expression recognition network with label distribution training. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, 2021;pp. 3510–3519
Zhao Z, Li Q, Zhang Z, Cummins N, Wang H, Tao J, Schuller BW. Combining a parallel 2D CNN with a self-attention Dilated Residual Network for CTC-based discrete speech emotion recognition. Neural Netw. 2021;141:52–60.
Woo S, Park J, Lee JY, Kweon IS. Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision, 2018;pp. 3–19
Tong L, Zhao J, Fu W. Emotion recognition and channel selection based on EEG signal. In: 2018 11th International Conference on Intelligent Computation Technology and Automation, 2018;pp. 101–105
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(1):382–93.
Duan R-N, Zhu J-Y, Lu B-L. Differential entropy feature for EEG-based emotion classification. In: 2013 6th International IEEE/EMBS Conference on Neural Engineering, 2013;pp. 81–84
Shi L-C, Jiao Y-Y, Lu B-L. Differential entropy feature for EEG-based vigilance estimation. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013;pp. 6627–6630
Wang Y, Zhang B, Tang Y. DMMR: Cross-subject domain generalization for EEG-based emotion recognition via denoising mixed mutual reconstruction. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2024;pp. 628–636
Koelstra S, Muhl C, Soleymani M, Lee JS, 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(1):18–31.
Ekman P. An argument for basic emotions. Cogn Emot. 1992;6(3–4):169–200.
Russell JA. Affective space is bipolar. J Pers Soc Psychol. 1979;37(3):345–56.
Article MathSciNet Google Scholar
Lee M, Park H-Y, Park W, Kim K-T, Kim Y-H, Jeong J-H. Multi-task heterogeneous ensemble learning-based cross-subject EEG classification under stroke patients. Eng: IEEE Trans. Neural Syst. Rehabil; 2024.
Kang H, Choi JW, Kim BH. Cascading global and sequential temporal representations with local context modeling for EEG-based emotion recognition. In: International Conference on Pattern Recognition, 2024;pp. 305–320
Li M, Lu BL. Emotion classification based on gamma-band EEG. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009;pp. 1223–1226
Lundberg SM, Lee SI. A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, 2017;vol. 30
Huang D, Guan C, Ang KK, Zhang H, Pan Y. Asymmetric spatial pattern for EEG-based emotion detection. In: the 2012 International Joint Conference on Neural Networks, 2012;pp. 1–7
Park HM, Kim G, Oh J, Messem AV, Neve WD. Interpreting stress detection models using SHAP and attention for MuSe-Stress 2022. IEEE Transactions on Affective Computing 2024.
Wang Y, Zhang B, Tang Y. DMMR: Cross-subject domain generalization for EEG-based emotion recognition via denoising mixed mutual reconstruction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, 2024;pp. 628–636
Wang K, He B, Zhu WP. TSTNN: Two-stage transformer based neural network for speech enhancement in the time domain. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021;pp. 7098–7102
Gu A, Dao T. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 2023.
Comments (0)