{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T17:19:41Z","timestamp":1792689581872,"version":"4.5.0"},"reference-count":27,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["4244100"],"award-info":[{"award-number":["4244100"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["62406324"],"award-info":[{"award-number":["62406324"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China","award":["4244100"],"award-info":[{"award-number":["4244100"]}]},{"name":"National Natural Science Foundation of China","award":["62406324"],"award-info":[{"award-number":["62406324"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sciences"],"abstract":"<jats:p>Background\/Objectives: Electroencephalography (EEG)-based emotion recognition plays an important role in affective computing and brain\u2013computer interface applications. However, existing methods often face the challenge of achieving high classification accuracy while maintaining physiological interpretability. Methods: In this study, we propose a convolutional neural network (CNN) model with a simple architecture for EEG-based emotion classification. The model achieves classification accuracies of 95.21% for low\/high arousal, 94.59% for low\/high valence, and 93.01% for quaternary classification tasks on the DEAP dataset. To further improve model interpretability and support practical applications, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to identify the EEG electrode regions that contribute most to the classification results. Results: The visualization reveals that electrodes located in the right prefrontal cortex and left parietal lobe are the most influential, which is consistent with findings from emotional lateralization theory. Conclusions: This provides a physiological basis for optimizing electrode placement in wearable EEG-based emotion recognition systems. The proposed method combines high classification performance with interpretability and provides guidance for the design of efficient and portable affective computing systems.<\/jats:p>","DOI":"10.3390\/brainsci15080886","type":"journal-article","created":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T07:50:46Z","timestamp":1755676246000},"page":"886","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Interpretable EEG Emotion Classification via CNN Model and Gradient-Weighted Class Activation Mapping"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7506-1703","authenticated-orcid":false,"given":"Yuxuan","family":"Zhao","sequence":"first","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linjing","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Software and Microelectronics, Peking University, Beijing 102600, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3816-0045","authenticated-orcid":false,"given":"Yidao","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5596-1795","authenticated-orcid":false,"given":"Bo","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0748-4952","authenticated-orcid":false,"given":"Wei","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sharma, R., Pachori, R.B., and Sircar, P. (2020). Automated emotion recognition based on higher order statistics and deep learning algorithm. Biomed. Signal Process. Control, 58.","DOI":"10.1016\/j.bspc.2020.101867"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106954","DOI":"10.1016\/j.asoc.2020.106954","article-title":"EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM","volume":"100","author":"Yin","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1109\/LSP.2022.3179946","article-title":"EEG-GCN: Spatio-temporal and self-adaptive graph convolutional networks for single and multi-view EEG-based emotion recognition","volume":"29","author":"Gao","year":"2022","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","article-title":"Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks","volume":"7","year":"2015","journal-title":"IEEE Trans. Auton. Ment. Dev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","article-title":"DEAP: A Database for Emotion Analysis Using Physiological Signals","volume":"3","author":"Koelstra","year":"2012","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106624","DOI":"10.1016\/j.neunet.2024.106624","article-title":"Emotion recognition using hierarchical spatial-temporal learning transformer from regional to global brain","volume":"179","author":"Cheng","year":"2024","journal-title":"Neural Netw."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, T., Huang, X., Xiao, Z., Cai, W., and Tai, Y. (2024). EEG emotion recognition based on differential entropy feature matrix through 2D-CNN-LSTM network. EURASIP J. Adv. Signal Process., 1\u201319.","DOI":"10.1186\/s13634-024-01146-y"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Houssein, E.H., Hammad, A., Emam, M.M., and Ali, A.A. (2024). An enhanced Coati Optimization Algorithm for global optimization and feature selection in EEG emotion recognition. Comput. Biol. Med., 173.","DOI":"10.1016\/j.compbiomed.2024.108329"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Celebi, M., Ozturk, S., and Kaplan, K. (2024). An emotion recognition method based on EWT-3D-CNN-BiLSTM-GRU-AT model. Comput. Biol. Med., 169.","DOI":"10.1016\/j.compbiomed.2024.107954"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Yang, J., Lin, J., Yu, D., and Cao, X. (2020, January 19\u201324). A 3D convolutional neural network for emotion recognition based on EEG signals. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9207420"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1109\/TAFFC.2021.3093923","article-title":"Multimodal Affective States Recognition Based on Multiscale CNNs and Biologically Inspired Decision Fusion Model","volume":"14","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual explanations from deep networks via gradient-based localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, Y., Wu, Q., Qiu, M., Wang, Y., and Chen, X. (2018, January 8\u201313). Emotion Recognition from Multi-Channel EEG through Parallel Convolutional Recurrent Neural Network. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489331"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2016, January 27\u201330). Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref_15","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). Tensorflow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1093\/brain\/awx031","article-title":"Effects of prefrontal cortex damage on emotion understanding: EEG and behavioural evidence","volume":"140","author":"Perry","year":"2017","journal-title":"Brain"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/j.biopsycho.2009.08.010","article-title":"The role of asymmetric frontal cortical activity in emotion-related phenomena: A review and update","volume":"84","author":"Gable","year":"2010","journal-title":"Biol. Psychol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102156","DOI":"10.1016\/j.inffus.2023.102156","article-title":"Multi-view domain-adaptive representation learning for EEG-based emotion recognition","volume":"104","author":"Li","year":"2024","journal-title":"Inf. Fusion"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ghosh, S.M., Bandyopadhvay, S., and Mitra, D. (2023, January 29\u201331). Emotion Recognition from Eeg Signals Based on Optimization of Mutual Information. Proceedings of the 2023 International Symposium on Devices, Circuits and Systems (ISDCS), Online.","DOI":"10.1109\/ISDCS58735.2023.10153567"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2266","DOI":"10.1109\/JSEN.2018.2883497","article-title":"Cross-Subject Emotion Recognition Using Flexible Analytic Wavelet Transform From EEG Signals","volume":"19","author":"Gupta","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1007\/s11760-019-01455-y","article-title":"A novel EEG-based approach to classify emotions through phase space dynamics","volume":"13","author":"Maghooli","year":"2019","journal-title":"Signal Image Video Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s12559-024-10361-6","article-title":"Cognitive-Inspired Spectral Spatiotemporal Analysis for Emotion Recognition Utilizing Electroencephalography Signals","volume":"17","author":"Goshvarpour","year":"2024","journal-title":"Cogn. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"27537","DOI":"10.1109\/ACCESS.2025.3539502","article-title":"Fine-Tuning EEG Channel Utilization for Emotionally Stimulated Biometric Authentication","volume":"13","author":"Rakshe","year":"2025","journal-title":"IEEE Access"},{"key":"ref_24","first-page":"479","article-title":"AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups","volume":"12","author":"Abadi","year":"2018","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/T-AFFC.2011.25","article-title":"A Multimodal Database for Affect Recognition and Implicit Tagging","volume":"3","author":"Soleymani","year":"2012","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Zhao, Y., Zhang, T., Zhao, D., Zhao, F., and Lu, E. (2020). A Brain-Inspired Model of Theory of Mind. Front. Neurorobot., 14.","DOI":"10.3389\/fnbot.2020.00060"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhao, Y., and Zeng, Y. (2022). A brain-inspired intention prediction model and its applications to humanoid robot. Front. Neurosci., 16.","DOI":"10.3389\/fnins.2022.1009237"}],"container-title":["Brain Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3425\/15\/8\/886\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:31:25Z","timestamp":1760034685000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3425\/15\/8\/886"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,20]]},"references-count":27,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["brainsci15080886"],"URL":"https:\/\/doi.org\/10.3390\/brainsci15080886","relation":{},"ISSN":["2076-3425"],"issn-type":[{"value":"2076-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,20]]}}}