{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T01:29:19Z","timestamp":1764811759623,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819709021"},{"type":"electronic","value":"9789819709038"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-0903-8_11","type":"book-chapter","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T11:02:50Z","timestamp":1709204570000},"page":"101-112","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Domain Adaptation Deep Learning Network for EEG-Based Motor Imagery Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6134-8404","authenticated-orcid":false,"given":"Jie","family":"Jiao","sequence":"first","affiliation":[]},{"given":"Yijie","family":"Pan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2266-4724","authenticated-orcid":false,"given":"Hefan","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5933-8469","authenticated-orcid":false,"given":"Qingqing","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7702-5642","authenticated-orcid":false,"given":"Wangliang","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Peipei","family":"Gu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2838-9145","authenticated-orcid":false,"given":"Meiyan","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,1]]},"reference":[{"issue":"4","key":"11_CR1","doi-asserted-by":"publisher","first-page":"1801","DOI":"10.1109\/JBHI.2023.3238421","volume":"27","author":"Y Kwak","year":"2023","unstructured":"Kwak, Y., Kong, K., Song, W.J., Kim, S.E.: Subject-invariant deep neural networks based on baseline correction for EEG motor imagery BCI. IEEE J. Biomed. Health Inform. 27(4), 1801\u20131812 (2023)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"11_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110292","volume":"263","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Ding, W.: Motor imagery classification via stacking-based takagi\u2013sugeno\u2013kang fuzzy classifier ensemble. Knowl.-Based Syst. 263, 110292 (2023)","journal-title":"Knowl.-Based Syst."},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Ai, J., Meng, J., Mai, X., Zhu, X.: Bci control of a robotic arm based on ssvep with moving stimuli for reach and grasp tasks. IEEE J. Biomed. Health Inform. (2023)","DOI":"10.1109\/JBHI.2023.3277612"},{"key":"11_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104799","volume":"137","author":"NK Al-Qazzaz","year":"2021","unstructured":"Al-Qazzaz, N.K., Alyasseri, Z.A.A., Abdulkareem, K.H., Ali, N.S., Al-Mhiqani, M.N., Guger, C.: EEG feature fusion for motor imagery: a new robust framework towards stroke patients rehabilitation. Comput. Biol. Med. 137, 104799 (2021)","journal-title":"Comput. Biol. Med."},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Amini, M.M., Shalchyan, V.: Designing a motion-onset visual evoked potential-based brain-computer interface to control a computer game. IEEE Trans. Games (2023)","DOI":"10.1109\/TG.2023.3279289"},{"issue":"6","key":"11_CR6","doi-asserted-by":"publisher","first-page":"2504","DOI":"10.1109\/JBHI.2022.3146274","volume":"26","author":"H Fang","year":"2022","unstructured":"Fang, H., Jin, J., Daly, I., Wang, X.: Feature extraction method based on filter banks and riemannian tangent space in motor-imagery BCI. IEEE J. Biomed. Health Inform. 26(6), 2504\u20132514 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"11_CR7","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/MCI.2018.2881647","volume":"14","author":"LW Ko","year":"2019","unstructured":"Ko, L.W., et al.: Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface. IEEE Comput. Intell. Mag. 14(1), 96\u2013106 (2019)","journal-title":"IEEE Comput. Intell. Mag."},{"key":"11_CR8","doi-asserted-by":"publisher","first-page":"2283","DOI":"10.1109\/TNSRE.2022.3198041","volume":"30","author":"H Zhu","year":"2022","unstructured":"Zhu, H., Forenzo, D., He, B.: On the deep learning models for EEG-based brain-computer interface using motor imagery. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 2283\u20132291 (2022)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"5","key":"11_CR9","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., Gordon, S.M., Hung, C.P., Lance, B.J.: EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces. J. Neural Eng. 15(5), 056013 (2018)","journal-title":"J. Neural Eng."},{"key":"11_CR10","unstructured":"Mane, R., et al.: FBCNet: a multi-view convolutional neural network for brain-computer interface. arXiv preprint, 2104.01233 (2021)"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Gao, D., Wang, K., Wang, M., Zhou, J., Zhang, Y.: SFT-net: a network for detecting fatigue from EEG signals by combining 4d feature flow and attention mechanism. IEEE J. Biomed. Health Inform. (2023)","DOI":"10.1109\/JBHI.2023.3285268"},{"issue":"10","key":"11_CR12","doi-asserted-by":"publisher","first-page":"2164","DOI":"10.1109\/TNSRE.2019.2938295","volume":"27","author":"X Zhao","year":"2019","unstructured":"Zhao, X., Zhang, H., Zhu, G., You, F., Kuang, S., Sun, L.: A multi-branch 3D convolutional neural network for EEG-based motor imagery classification. IEEE Trans. Neural Syst. Rehabil. Eng. 27(10), 2164\u20132177 (2019)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"11_CR13","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1109\/TNSRE.2022.3147673","volume":"30","author":"D Chen","year":"2022","unstructured":"Chen, D., et al.: Scalp EEG-based pain detection using convolutional neural network. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 274\u2013285 (2022)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Cui, J., Lan, Z., Sourina, O., M\u00fcller-Wittig, W.: EEG-based cross-subject driver drowsiness recognition with an interpretable convolutional neural network. IEEE Trans. Neural Networks Learn. Syst. (2022)","DOI":"10.1109\/TNNLS.2022.3147208"},{"key":"11_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126659","volume":"556","author":"X Zhang","year":"2023","unstructured":"Zhang, X., Miao, Z., Menon, C., Zheng, Y., Zhao, M., Ming, D.: Priming cross-session motor imagery classification with a universal deep domain adaptation framework. Neurocomputing 556, 126659 (2023)","journal-title":"Neurocomputing"},{"issue":"9","key":"11_CR16","doi-asserted-by":"publisher","first-page":"2570","DOI":"10.1109\/JBHI.2020.2967128","volume":"24","author":"D Zhang","year":"2020","unstructured":"Zhang, D., Chen, K., Jian, D., Yao, L.: Motor imagery classification via temporal attention cues of graph embedded EEG signals. IEEE J. Biomed. Health Inform. 24(9), 2570\u20132579 (2020)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Altaheri, H., Muhammad, G., Alsulaiman, M.: Dynamic convolution with multi-level attention for eeg-based motor imagery decoding. IEEE Internet Things J. 1 (2023)","DOI":"10.1109\/JIOT.2023.3281911"},{"key":"11_CR18","doi-asserted-by":"publisher","first-page":"1743","DOI":"10.1109\/TNSRE.2023.3259730","volume":"31","author":"A Li","year":"2023","unstructured":"Li, A., Wang, Z., Zhao, X., Xu, T., Zhou, T., Hu, H.: MDTL: a novel and model-agnostic transfer learning strategy for cross-subject motor imagery BCI. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 1743\u20131753 (2023)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Jia, Z., Lin, Y., Cai, X., Chen, H., Gou, H., Wang, J.: Sst-emotionnet: spatial-spectral-temporal based attention 3d dense network for EEG emotion recognition. In: Proceedings of the 28th ACM International Conference on Multimedia. pp. 2909\u20132917 (2020)","DOI":"10.1145\/3394171.3413724"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Lee, M.H., et al.: EEG dataset and OpenBMI toolbox for three BCI paradigms: An investigation into BCI illiteracy. GigaScience 8(5), giz002 (2019)","DOI":"10.1093\/gigascience\/giz002"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Tangermann, M., et al.: Review of the BCI competition IV. Front. Neurosci. p. 55 (2012)","DOI":"10.3389\/fnins.2012.00055"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Autthasan, P., et al.: Min2net: end-to-end multi-task learning for subject-independent motor imagery EEG classification. IEEE Trans. Biomed. Eng. 69(6), 2105\u20132118 (2021)","DOI":"10.1109\/TBME.2021.3137184"},{"key":"11_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neunet.2020.12.013","volume":"136","author":"K Zhang","year":"2021","unstructured":"Zhang, K., Robinson, N., Lee, S.W., Guan, C.: Adaptive transfer learning for EEG motor imagery classification with deep convolutional neural network. Neural Netw. 136, 1\u201310 (2021)","journal-title":"Neural Netw."},{"issue":"11","key":"11_CR25","doi-asserted-by":"publisher","first-page":"5391","DOI":"10.1002\/hbm.23730","volume":"38","author":"RT Schirrmeister","year":"2017","unstructured":"Schirrmeister, R.T., et al.: Deep learning with convolutional neural networks for EEG decoding and visualization. Hum. Brain Mapp. 38(11), 5391\u20135420 (2017)","journal-title":"Hum. Brain Mapp."}],"container-title":["Communications in Computer and Information Science","Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-0903-8_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T11:12:29Z","timestamp":1709205149000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-0903-8_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819709021","9789819709038"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-0903-8_11","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applied Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanning","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icai12023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icai.org.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}