{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T21:17:55Z","timestamp":1773695875311,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"S1","license":[{"start":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T00:00:00Z","timestamp":1713830400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T00:00:00Z","timestamp":1713830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62072468"],"award-info":[{"award-number":["No. 62072468"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s11760-024-03178-1","type":"journal-article","created":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T06:02:01Z","timestamp":1713852121000},"page":"617-626","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Emotion recognition with attention mechanism-guided dual-feature multi-path interaction network"],"prefix":"10.1007","volume":"18","author":[{"given":"Yaxuan","family":"Li","sequence":"first","affiliation":[]},{"given":"Wenhui","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Yanjiang","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,23]]},"reference":[{"issue":"1","key":"3178_CR1","doi-asserted-by":"publisher","first-page":"498","DOI":"10.3390\/s23010498","volume":"23","author":"FM Alotaibi","year":"2023","unstructured":"Alotaibi, F.M.: An AI-inspired spatio-temporal neural network for EEG-based emotional status. Sensors 23(1), 498 (2023)","journal-title":"Sensors"},{"key":"3178_CR2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45, 5\u201332 (2001)","journal-title":"Mach. Learn."},{"issue":"1","key":"3178_CR3","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21\u201327 (1967)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"3178_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106243","volume":"205","author":"H Cui","year":"2020","unstructured":"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. 205, 106243 (2020)","journal-title":"Knowl. Based Syst."},{"key":"3178_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2022.127700","volume":"603","author":"JY Guo","year":"2022","unstructured":"Guo, J.Y., Cai, Q., An, J.P., Chen, P.Y., Ma, C., Wan, J.H., Gao, Z.K.: A transformer based neural network for emotion recognition and visualizations of crucial EEG channels. Physica A 603, 127700 (2022)","journal-title":"Physica A"},{"key":"3178_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104998","volume":"84","author":"W Guo","year":"2023","unstructured":"Guo, W., Xu, G., Wang, Y.: Multi-source domain adaptation with spatio-temporal feature extractor for EEG emotion recognition. Biomed. Signal Process. Control 84, 104998 (2023)","journal-title":"Biomed. Signal Process. Control"},{"issue":"4","key":"3178_CR7","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/5254.708428","volume":"13","author":"M Hearst","year":"1998","unstructured":"Hearst, M., Dumais, S., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Their Appl. 13(4), 18\u201328 (1998)","journal-title":"IEEE Intell. Syst. Their Appl."},{"issue":"8","key":"3178_CR8","doi-asserted-by":"publisher","first-page":"987","DOI":"10.3390\/brainsci12080987","volume":"12","author":"J Jia","year":"2022","unstructured":"Jia, J., Zhang, B., Lv, H., Xu, Z., Hu, S., Li, H.: CR-GCN: Channel-relationships-based graph convolutional network for EEG emotion recognition. Brain Sci. 12(8), 987 (2022)","journal-title":"Brain Sci."},{"key":"3178_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2022.112512","volume":"162","author":"S Jothimani","year":"2022","unstructured":"Jothimani, S., Premalatha, K.: MFF-SAug: Multi feature fusion with spectrogram augmentation of speech emotion recognition using convolution neural network. Chaos Solit. Fractals 162, 112512 (2022)","journal-title":"Chaos Solit. Fractals"},{"issue":"16","key":"3178_CR10","doi-asserted-by":"publisher","first-page":"13291","DOI":"10.1007\/s00521-022-06942-x","volume":"34","author":"N Kumari","year":"2022","unstructured":"Kumari, N., Anwar, S., Bhattacharjee, V.: Time series-dependent feature of EEG signals for improved visually evoked emotion classification using EmotionCapsNet. Neural Comput. Appl. 34(16), 13291\u201313303 (2022)","journal-title":"Neural Comput. Appl."},{"key":"3178_CR11","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.neucom.2021.02.048","volume":"447","author":"Y Li","year":"2021","unstructured":"Li, Y., Fu, B., Li, F., Shi, G., Zheng, W.: A novel transferability attention neural network model for EEG emotion recognition. Neurocomputing 447, 92\u2013101 (2021)","journal-title":"Neurocomputing"},{"issue":"2","key":"3178_CR12","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1109\/TCDS.2020.2999337","volume":"13","author":"Y Li","year":"2020","unstructured":"Li, Y., Wang, L., Zheng, W., Zong, Y., Qi, L., Cui, Z., Zhang, T., Song, T.: A novel bi-hemispheric discrepancy model for EEG emotion recognition. IEEE Trans. Cogn. Dev. Syst. 13(2), 354\u2013367 (2020)","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"issue":"2","key":"3178_CR13","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1109\/TAFFC.2018.2885474","volume":"12","author":"Y Li","year":"2018","unstructured":"Li, Y., Zheng, W., Zong, Y., Cui, Z., Zhang, T., Zhou, X.: A bi-hemisphere domain adversarial neural network model for EEG emotion recognition. IEEE Trans. Affect. Comput. 12(2), 494\u2013504 (2018)","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"12","key":"3178_CR14","doi-asserted-by":"publisher","first-page":"5964","DOI":"10.1109\/JBHI.2022.3210158","volume":"26","author":"Z Li","year":"2022","unstructured":"Li, Z., Zhu, E., Jin, M., Fan, C., He, H., Cai, T., Li, J.: Dynamic domain adaptation for class-aware cross-subject and cross-session EEG emotion recognition. IEEE J. Biomed. Health Inform. 26(12), 5964\u20135973 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"3178_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110372","volume":"265","author":"S Liu","year":"2023","unstructured":"Liu, S., Wang, Z., An, Y., Zhao, J., Zhao, Y., Zhang, Y.D.: EEG emotion recognition based on the attention mechanism and pre-trained convolution capsule network. Knowl. Based Syst. 265, 110372 (2023)","journal-title":"Knowl. Based Syst."},{"key":"3178_CR16","doi-asserted-by":"crossref","unstructured":"Mehmood, R.M., Lee, H.J.: Emotion classification of EEG brain signal using SVM and KNN. In: 2015 IEEE International Conference On Multimedia and Expo Workshops (ICMEW), pp.\u00a01\u20135. IEEE (2015)","DOI":"10.1109\/ICMEW.2015.7169786"},{"key":"3178_CR17","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1016\/j.ins.2022.06.092","volume":"608","author":"H Sadeghi","year":"2022","unstructured":"Sadeghi, H., Raie, A.A.: HistNet: histogram-based convolutional neural network with chi-squared deep metric learning for facial expression recognition. Inf. Sci. 608, 472\u2013488 (2022)","journal-title":"Inf. Sci."},{"key":"3178_CR18","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1007\/s11571-020-09634-1","volume":"14","author":"F Shen","year":"2020","unstructured":"Shen, F., Dai, G., Lin, G., Zhang, J., Kong, W., Zeng, H.: EEG-based emotion recognition using 4d convolutional recurrent neural network. Cogn. Neurodyn. 14, 815\u2013828 (2020)","journal-title":"Cogn. Neurodyn."},{"issue":"3","key":"3178_CR19","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/TAFFC.2018.2817622","volume":"11","author":"T Song","year":"2018","unstructured":"Song, T., Zheng, W., Song, P., Cui, Z.: EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans. Affect. Comput. 11(3), 532\u2013541 (2018)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"3178_CR20","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1023\/A:1018628609742","volume":"9","author":"JA Suykens","year":"1999","unstructured":"Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9, 293\u2013300 (1999)","journal-title":"Neural Process. Lett."},{"key":"3178_CR21","doi-asserted-by":"crossref","unstructured":"Tang, H., Liu, W., Zheng, W.L., Lu, B.L.: Multimodal emotion recognition using deep neural networks. In: Neural Information Processing: 24th International Conference, ICONIP 2017, Guangzhou, China, November 14\u201318, 2017, Proceedings, Part IV 24, pp. 811\u2013819. Springer (2017)","DOI":"10.1007\/978-3-319-70093-9_86"},{"key":"3178_CR22","unstructured":"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. (2020)"},{"key":"3178_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuropsychologia.2020.107506","volume":"146","author":"F Wang","year":"2020","unstructured":"Wang, F., Wu, S., Zhang, W., Xu, Z., Zhang, Y., Wu, C., Coleman, S.: Emotion recognition with convolutional neural network and EEG-based EFDMs. Neuropsychologia 146, 107506 (2020)","journal-title":"Neuropsychologia"},{"key":"3178_CR24","doi-asserted-by":"crossref","unstructured":"Xiao, G., Shi, M., Ye, M., Xu, B., Chen, Z., Ren, Q.: 4D attention-based neural network for EEG emotion recognition. Cogn. Neurodyn. 1\u201314 (2022)","DOI":"10.1007\/s11571-021-09751-5"},{"issue":"5","key":"3178_CR25","doi-asserted-by":"publisher","first-page":"2787","DOI":"10.1109\/TCYB.2019.2925095","volume":"51","author":"W Xie","year":"2021","unstructured":"Xie, W., Shen, L., Duan, J.: Adaptive weighting of handcrafted feature losses for facial expression recognition. IEEE Trans. Cybern. 51(5), 2787\u20132800 (2021)","journal-title":"IEEE Trans. Cybern."},{"key":"3178_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106217","volume":"204","author":"L Yang","year":"2020","unstructured":"Yang, L., Tian, Y., Song, Y., Yang, N., Ma, K., Xie, L.: A novel feature separation model exchange-GAN for facial expression recognition. Knowl. Based Syst. 204, 106217 (2020)","journal-title":"Knowl. Based Syst."},{"key":"3178_CR27","doi-asserted-by":"crossref","unstructured":"Yang, L., Yang, H., Hu, B.B., Wang, Y., Lv, C.: A robust driver emotion recognition method based on high-purity feature separation. IEEE Trans. Intell. Transp. Syst. (2023)","DOI":"10.1109\/TITS.2023.3304128"},{"key":"3178_CR28","doi-asserted-by":"crossref","unstructured":"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, ICONIP 2018, Siem Reap, Cambodia, December 13\u201316, 2018, Proceedings, Part VII 25, pp. 433\u2013443. Springer (2018)","DOI":"10.1007\/978-3-030-04239-4_39"},{"key":"3178_CR29","doi-asserted-by":"crossref","unstructured":"Yang, Y., Wang, D., Zheng, Y., Yang, Y.: EEG-based emotion recognition using multi-dimensional convolutional neural LSTM via attention mechanism. In: 2023 IEEE 6th International Electrical and Energy Conference (CIEEC), pp. 579\u2013584. IEEE (2023)","DOI":"10.1109\/CIEEC58067.2023.10167160"},{"issue":"12","key":"3178_CR30","doi-asserted-by":"publisher","first-page":"11954","DOI":"10.1109\/JSEN.2022.3172133","volume":"22","author":"Q Yao","year":"2022","unstructured":"Yao, Q., Gu, H., Wang, S., Li, X.: A feature-fused convolutional neural network for emotion recognition from multichannel EEG signals. IEEE Sens. J. 22(12), 11954\u201311964 (2022)","journal-title":"IEEE Sens. J."},{"key":"3178_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108401","volume":"123","author":"W Yu","year":"2022","unstructured":"Yu, W., Xu, H.: Co-attentive multi-task convolutional neural network for facial expression recognition. Pattern Recogn. 123, 108401 (2022)","journal-title":"Pattern Recogn."},{"issue":"5","key":"3178_CR32","doi-asserted-by":"publisher","first-page":"2305","DOI":"10.1007\/s11760-022-02447-1","volume":"17","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Zhang, Y., Wang, S.: An attention-based hybrid deep learning model for EEG emotion recognition. SIViP 17(5), 2305\u20132313 (2023)","journal-title":"SIViP"},{"issue":"6","key":"3178_CR33","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/acd675","volume":"44","author":"F Zheng","year":"2023","unstructured":"Zheng, F., Hu, B., Zheng, X., Zhang, Y.: Spatial-temporal features-based EEG emotion recognition using graph convolution network and long short-term memory. Physiol. Meas. 44(6), 065002 (2023)","journal-title":"Physiol. Meas."},{"issue":"3","key":"3178_CR34","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","volume":"7","author":"WL Zheng","year":"2015","unstructured":"Zheng, W.L., Lu, B.L.: Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans. Auton. Ment. Dev. 7(3), 162\u2013175 (2015)","journal-title":"IEEE Trans. Auton. Ment. Dev."},{"issue":"3","key":"3178_CR35","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1109\/TAFFC.2017.2712143","volume":"10","author":"WL Zheng","year":"2017","unstructured":"Zheng, W.L., Zhu, J.Y., Lu, B.L.: Identifying stable patterns over time for emotion recognition from EEG. IEEE Trans. Affect. Comput. 10(3), 417\u2013429 (2017)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"3178_CR36","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Asghar, M.A., Nazir, D., Siddique, K., Shorfuzzaman, M., Mehmood, R.M.: An AI-empowered affect recognition model for healthcare and emotional well-being using physiological signals. Clust. Comput. 26(2), 1253\u20131266 (2023)","DOI":"10.1007\/s10586-022-03705-0"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03178-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-024-03178-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03178-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T12:26:04Z","timestamp":1719318364000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-024-03178-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,23]]},"references-count":36,"journal-issue":{"issue":"S1","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["3178"],"URL":"https:\/\/doi.org\/10.1007\/s11760-024-03178-1","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,23]]},"assertion":[{"value":"6 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 March 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}