{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T10:36:20Z","timestamp":1769855780856,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819500321","type":"print"},{"value":"9789819500338","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-95-0033-8_10","type":"book-chapter","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T08:01:55Z","timestamp":1753344115000},"page":"111-122","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dynamic Bidirectional Attentional Mamba Model for EEG-Based Motor Imagery Classification"],"prefix":"10.1007","author":[{"given":"Bin","family":"Liu","sequence":"first","affiliation":[]},{"given":"Qianzi","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Zijian","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yanting","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Cairong","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,25]]},"reference":[{"issue":"1","key":"10_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rehab.2020.03.015","volume":"64","author":"D Wen","year":"2021","unstructured":"Wen, D., Fan, Y., Hsu, S.H., et al.: Combining brain\u2013computer interface and virtual reality for rehabilitation in neurological diseases: a narrative review. Ann. Phys. Rehabil. Med. 64(1), 101404 (2021)","journal-title":"Ann. Phys. Rehabil. Med."},{"issue":"10","key":"10_CR2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pbio.3000479","volume":"17","author":"S Little","year":"2019","unstructured":"Little, S., Bonaiuto, J., Barnes, G., et al.: Human motor cortical beta bursts relate to movement planning and response errors. PLoS Biol. 17(10), e3000479 (2019)","journal-title":"PLoS Biol."},{"key":"10_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2019.101684","volume":"22","author":"A Emami","year":"2019","unstructured":"Emami, A., Kunii, N., Matsuo, T., et al.: Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images. NeuroImage Clin. 22, 101684 (2019)","journal-title":"NeuroImage Clin."},{"key":"10_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102203","volume":"63","author":"F Li","year":"2021","unstructured":"Li, F., Yan, R., Mahini, R., et al.: End-to-end sleep staging using convolutional neural network in raw single-channel EEG. Biomed. Sig. Process. Control 63, 102203 (2021)","journal-title":"Biomed. Sig. Process. Control"},{"key":"10_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102393","volume":"66","author":"G Sharma","year":"2021","unstructured":"Sharma, G., Parashar, A., Joshi, A.M.: Dephnn: a novel hybrid neural network for electroencephalogram (Eeg)-based screening of depression. Biomed. Signal Process. Control 66, 102393 (2021)","journal-title":"Biomed. Signal Process. Control"},{"key":"10_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108504","volume":"175","author":"X Ma","year":"2024","unstructured":"Ma, X., Chen, W., Pei, Z., et al.: Attention-based convolutional neural network with multi-modal temporal information fusion for motor imagery EEG decoding. Comput. Biol. Med. 175, 108504 (2024)","journal-title":"Comput. Biol. Med."},{"issue":"21","key":"10_CR7","doi-asserted-by":"publisher","first-page":"18579","DOI":"10.1109\/JIOT.2023.3281911","volume":"10","author":"H Altaheri","year":"2023","unstructured":"Altaheri, H., Muhammad, G., Alsulaiman, M.: Dynamic convolution with multi-level attention for eeg-based motor imagery decoding. IEEE Internet Things J. 10(21), 18579\u201318588 (2023)","journal-title":"IEEE Internet Things J."},{"key":"10_CR8","doi-asserted-by":"publisher","first-page":"74930","DOI":"10.1109\/ACCESS.2024.3404634","volume":"12","author":"K Zhou","year":"2024","unstructured":"Zhou, K., Haimudula, A., Tang, W.: Dual-branch convolution network with efficient channel attention for EEG-based motor imagery classification. IEEE Access 12, 74930\u201374943 (2024)","journal-title":"IEEE Access"},{"key":"10_CR9","first-page":"572","volume":"34","author":"A Gu","year":"2021","unstructured":"Gu, A., Johnson, I., Goel, K., et al.: Combining recurrent, convolutional, and continuous-time models with linear state space layers. Adv. Neural. Inf. Process. Syst. 34, 572\u2013585 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR10","unstructured":"Gu, A., Dao, T.: Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 (2023)"},{"key":"10_CR11","unstructured":"Zhu, L., Liao, B., Zhang, Q., et al.: Vision mamba: efficient visual representation learning with bidirectional state space model. In: Forty-First International Conference on Machine Learning (2024)"},{"key":"10_CR12","unstructured":"Brunner, C., Leeb, R., M\u00fcller-Putz, G., et al.: BCI competition 2008\u2013Graz data set a. Institute for knowledge discovery (laboratory of brain-computer interfaces), Graz Univ. Technol. 16, 1\u20136 (2008)"},{"issue":"5","key":"10_CR13","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aace8c","volume":"15","author":"VJ Lawhern","year":"2018","unstructured":"Lawhern, V.J., Solon, A.J., Waytowich, N.R., et al.: EEGNet: a compact convolutional neural network for EEG-based brain\u2013computer interfaces. J. Neural Eng. 15(5), 056013 (2018)","journal-title":"J. Neural Eng."},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., et al.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11534\u201311542 (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"issue":"2","key":"10_CR15","doi-asserted-by":"publisher","first-page":"2249","DOI":"10.1109\/TII.2022.3197419","volume":"19","author":"H Altaheri","year":"2023","unstructured":"Altaheri, H., Muhammad, G., Alsulaiman, M.: Physics-informed attention temporal convolutional network for EEG-based motor imagery classification. IEEE Trans. Ind. Inf. 19(2), 2249\u20132258 (2023)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102826","volume":"69","author":"YK Musallam","year":"2021","unstructured":"Musallam, Y.K., AlFassam, N.I., Muhammad, G., et al.: Electroencephalography-based motor imagery classification using temporal convolutional network fusion. Biomed. Sig. Process. Control 69, 102826 (2021)","journal-title":"Biomed. Sig. Process. Control"},{"key":"10_CR17","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(11) (2008)"},{"issue":"4","key":"10_CR18","doi-asserted-by":"publisher","first-page":"995","DOI":"10.3390\/diagnostics12040995","volume":"12","author":"GA Altuwaijri","year":"2022","unstructured":"Altuwaijri, G.A., Muhammad, G., Altaheri, H., et al.: A multi-branch convolutional neural network with squeeze-and-excitation attention blocks for EEG-based motor imagery signals classification. Diagnostics 12(4), 995 (2022)","journal-title":"Diagnostics"},{"issue":"1","key":"10_CR19","doi-asserted-by":"publisher","first-page":"2423","DOI":"10.1109\/TCE.2023.3330423","volume":"70","author":"Y Zhang","year":"2024","unstructured":"Zhang, Y., Li, P., Cheng, L., et al.: Attention-based multiscale spatial-temporal convolutional network for motor imagery EEG decoding. IEEE Trans. Consum. Electron. 70(1), 2423\u20132434 (2024)","journal-title":"IEEE Trans. Consum. Electron."},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Yang, X., Jia, Z.: Spatial-temporal Mamba network for EEG-based motor imagery classification. In: Advanced Data Mining and Applications, pp. 418\u2013432. Springer, Singapore (2024)","DOI":"10.1007\/978-981-96-0821-8_28"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-0033-8_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T21:25:18Z","timestamp":1757280318000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-0033-8_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819500321","9789819500338"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-0033-8_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}