{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T20:07:21Z","timestamp":1760731641726,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031702587"},{"type":"electronic","value":"9783031702594"}],"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\/978-3-031-70259-4_34","type":"book-chapter","created":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T09:02:15Z","timestamp":1725786135000},"page":"445-457","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Temporal Focal Modulation Networks for\u00a0EEG-Based Cross-Subject Motor Imagery Classification"],"prefix":"10.1007","author":[{"given":"Adel","family":"Hameed","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rahma","family":"Fourati","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boudour","family":"Ammar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Javier","family":"Sanchez-Medina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hela","family":"Ltifi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,9]]},"reference":[{"issue":"6","key":"34_CR1","doi-asserted-by":"publisher","first-page":"2105","DOI":"10.1109\/TBME.2021.3137184","volume":"69","author":"P Autthasan","year":"2021","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)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"6","key":"34_CR2","doi-asserted-by":"publisher","first-page":"2105","DOI":"10.1109\/TBME.2021.3137184","volume":"69","author":"P Autthasan","year":"2022","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 (2022). https:\/\/doi.org\/10.1109\/TBME.2021.3137184","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"34_CR3","unstructured":"Brunner, C., Leeb, R., M\u00fcller-Putz, G., Schl\u00f6gl, A., Pfurtscheller, G.: Bci competition 2008\u2013Graz data set A. Inst. Knowl. Disc. (Lab. Brain-Comput. Interfaces), Graz Univ. Technol. 16, 1\u20136 (2008)"},{"issue":"1","key":"34_CR4","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab405f","volume":"17","author":"G Dai","year":"2020","unstructured":"Dai, G., Zhou, J., Huang, J., Wang, N.: HS-CNN: a cnn with hybrid convolution scale for EEG motor imagery classification. J. Neural Eng. 17(1), 016025 (2020)","journal-title":"J. Neural Eng."},{"key":"34_CR5","doi-asserted-by":"crossref","unstructured":"Deny, P., Cheon, S., Son, H., Choi, K.W.: Hierarchical transformer for motor imagery-based brain computer interface. IEEE J. Biomed. Health Inf. (2023)","DOI":"10.1109\/JBHI.2023.3304646"},{"key":"34_CR6","doi-asserted-by":"publisher","first-page":"2238","DOI":"10.1109\/TAFFC.2022.3169001","volume":"14","author":"Y Ding","year":"2022","unstructured":"Ding, Y., Robinson, N., Zhang, S., Zeng, Q., Guan, C.: Tsception: capturing temporal dynamics and spatial asymmetry from EEG for emotion recognition. IEEE Trans. Affect. Comput. 14, 2238\u20132250 (2022)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"34_CR7","doi-asserted-by":"crossref","unstructured":"Dolzhikova, I., Abibullaev, B., Sameni, R., Zollanvari, A.: An ensemble CNN for subject-independent classification of motor imagery-based EEG. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 319\u2013324. IEEE (2021)","DOI":"10.1109\/EMBC46164.2021.9630419"},{"issue":"7","key":"34_CR8","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.3390\/brainsci13071109","volume":"13","author":"Y Dong","year":"2023","unstructured":"Dong, Y., et al.: Subject-independent EEG classification of motor imagery based on dual-branch feature fusion. Brain Sci. 13(7), 1109 (2023)","journal-title":"Brain Sci."},{"key":"34_CR9","doi-asserted-by":"crossref","unstructured":"d\u2019Ascoli, S., Touvron, H., Leavitt, M.L., Morcos, A.S., Biroli, G., Sagun, L.: Convit: improving vision transformers with soft convolutional inductive biases. In: International Conference on Machine Learning, pp. 2286\u20132296. PMLR (2021)","DOI":"10.1088\/1742-5468\/ac9830"},{"key":"34_CR10","doi-asserted-by":"crossref","unstructured":"Edelman, B.J., et al.: Noninvasive neuroimaging enhances continuous neural tracking for robotic device control. Sci. Rob. 4(31), eaaw6844 (2019)","DOI":"10.1126\/scirobotics.aaw6844"},{"issue":"2","key":"34_CR11","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1109\/TAFFC.2020.2982143","volume":"13","author":"R Fourati","year":"2022","unstructured":"Fourati, R., Ammar, B., Sanchez-Medina, J., Alimi, A.M.: Unsupervised learning in reservoir computing for EEG-based emotion recognition. IEEE Trans. Affect. Comput. 13(2), 972\u2013984 (2022). https:\/\/doi.org\/10.1109\/TAFFC.2020.2982143","journal-title":"IEEE Trans. Affect. Comput."},{"key":"34_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105359","volume":"87","author":"A Hameed","year":"2024","unstructured":"Hameed, A., et al.: Temporal-spatial transformer based motor imagery classification for BCI using independent component analysis. Biomed. Signal Process. Control 87, 105359 (2024)","journal-title":"Biomed. Signal Process. Control"},{"key":"34_CR13","doi-asserted-by":"publisher","first-page":"98275","DOI":"10.1109\/ACCESS.2021.3091399","volume":"9","author":"DM Hermosilla","year":"2021","unstructured":"Hermosilla, D.M., et al.: Shallow convolutional network excel for classifying motor imagery EEG in BCI applications. IEEE Access 9, 98275\u201398286 (2021)","journal-title":"IEEE Access"},{"key":"34_CR14","doi-asserted-by":"publisher","first-page":"1173778","DOI":"10.3389\/fnins.2023.1173778","volume":"17","author":"L Hu","year":"2023","unstructured":"Hu, L., Hong, W., Liu, L.: Msatnet: multi-scale adaptive transformer network for motor imagery classification. Front. Neurosci. 17, 1173778 (2023)","journal-title":"Front. Neurosci."},{"issue":"2","key":"34_CR15","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1109\/JBHI.2018.2832538","volume":"23","author":"Y Jiao","year":"2019","unstructured":"Jiao, Y., et al.: Sparse group representation model for motor imagery EEG classification. IEEE J. Biomed. Health Inform. 23(2), 631\u2013641 (2019). https:\/\/doi.org\/10.1109\/JBHI.2018.2832538","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"10","key":"34_CR16","doi-asserted-by":"publisher","first-page":"3839","DOI":"10.1109\/TNNLS.2019.2946869","volume":"31","author":"OY Kwon","year":"2019","unstructured":"Kwon, O.Y., Lee, M.H., Guan, C., Lee, S.W.: Subject-independent brain-computer interfaces based on deep convolutional neural networks. IEEE Trans. Neural Netw. Learn. Syst. 31(10), 3839\u20133852 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"5","key":"34_CR17","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":"34_CR18","unstructured":"Leeb, R., Brunner, C., M\u00fcller-Putz, G., Schl\u00f6gl, A., Pfurtscheller, G.: Bci competition 2008-graz data set b. Graz University of Technology, Austria, pp. 1\u20136 (2008)"},{"key":"34_CR19","doi-asserted-by":"publisher","unstructured":"Liang, G., Cao, D., Wang, J., Zhang, Z., Wu, Y.: Eisatc-fusion: inception self-attention temporal convolutional network fusion for motor imagery eeg decoding. TechRxiv (2023). https:\/\/doi.org\/10.36227\/techrxiv.24003582.v1","DOI":"10.36227\/techrxiv.24003582.v1"},{"key":"34_CR20","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"34_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107254","volume":"164","author":"J Luo","year":"2023","unstructured":"Luo, J., et al.: A shallow mirror transformer for subject-independent motor imagery BCI. Comput. Biol. Med. 164, 107254 (2023)","journal-title":"Comput. Biol. Med."},{"key":"34_CR22","doi-asserted-by":"crossref","unstructured":"Ma, Y., Song, Y., Gao, F.: A novel hybrid cnn-transformer model for EEG motor imagery classification. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20138. IEEE (2022)","DOI":"10.1109\/IJCNN55064.2022.9892821"},{"issue":"2","key":"34_CR23","doi-asserted-by":"publisher","first-page":"703","DOI":"10.3390\/s23020703","volume":"23","author":"D Milan\u00e9s-Hermosilla","year":"2023","unstructured":"Milan\u00e9s-Hermosilla, D., et al.: Robust motor imagery tasks classification approach using bayesian neural network. Sensors 23(2), 703 (2023)","journal-title":"Sensors"},{"key":"34_CR24","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/s11517-012-1018-1","volume":"51","author":"IK Niazi","year":"2013","unstructured":"Niazi, I.K., Jiang, N., Jochumsen, M., Nielsen, J.F., Dremstrup, K., Farina, D.: Detection of movement-related cortical potentials based on subject-independent training. Med. Biol. Eng. Comput. 51, 507\u2013512 (2013)","journal-title":"Med. Biol. Eng. Comput."},{"key":"34_CR25","doi-asserted-by":"crossref","unstructured":"Penaloza, C., Nishio, S.: Bmi control of a third arm for multitasking. Sci. Rob. 3(20), pmid: 33141729 (2018)","DOI":"10.1126\/scirobotics.aat1228"},{"key":"34_CR26","doi-asserted-by":"publisher","first-page":"918","DOI":"10.3389\/fnins.2020.00918","volume":"14","author":"S Roy","year":"2020","unstructured":"Roy, S., Chowdhury, A., McCreadie, K., Prasad, G.: Deep learning based inter-subject continuous decoding of motor imagery for practical brain-computer interfaces. Front. Neurosci. 14, 918 (2020)","journal-title":"Front. Neurosci."},{"issue":"10","key":"34_CR27","doi-asserted-by":"publisher","first-page":"1554","DOI":"10.1038\/s41593-019-0488-y","volume":"22","author":"MM Shanechi","year":"2019","unstructured":"Shanechi, M.M.: Brain-machine interfaces from motor to mood. Nat. Neurosci. 22(10), 1554\u20131564 (2019)","journal-title":"Nat. Neurosci."},{"key":"34_CR28","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1109\/TNSRE.2022.3230250","volume":"31","author":"Y Song","year":"2022","unstructured":"Song, Y., Zheng, Q., Liu, B., Gao, X.: EEG conformer: convolutional transformer for EEG decoding and visualization. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 710\u2013719 (2022)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"34_CR29","doi-asserted-by":"crossref","unstructured":"Tao, Y., et al.: Gated transformer for decoding human brain EEG signals. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 125\u2013130. IEEE (2021)","DOI":"10.1109\/EMBC46164.2021.9630210"},{"key":"34_CR30","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"34_CR31","first-page":"4203","volume":"35","author":"J Yang","year":"2022","unstructured":"Yang, J., Li, C., Dai, X., Gao, J.: Focal modulation networks. Adv. Neural. Inf. Process. Syst. 35, 4203\u20134217 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"5","key":"34_CR32","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1109\/LSP.2019.2906824","volume":"26","author":"D Zhang","year":"2019","unstructured":"Zhang, D., Yao, L., Chen, K., Monaghan, J.: A convolutional recurrent attention model for subject-independent EEG signal analysis. IEEE Signal Process. Lett. 26(5), 715\u2013719 (2019)","journal-title":"IEEE Signal Process. Lett."},{"key":"34_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, J., Li, K., Yang, B., Han, X.: Local and global convolutional transformer-based motor imagery EEG classification. Front. Neurosci. 17 (2023)","DOI":"10.3389\/fnins.2023.1219988"},{"key":"34_CR34","unstructured":"Zhang, N.: Learning adversarial transformer for symbolic music generation. IEEE Trans. Neural Netw. Learn. Syst. (2020)"},{"key":"34_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.medntd.2023.100215","volume":"17","author":"R Zhao","year":"2023","unstructured":"Zhao, R., et al.: A mutli-scale spatial-temporal convolutional neural network with contrastive learning for motor imagery EEG classification. Med. Novel Technol. Dev. 17, 100215 (2023)","journal-title":"Med. Novel Technol. Dev."}],"container-title":["Communications in Computer and Information Science","Advances in Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70259-4_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T09:19:54Z","timestamp":1725787194000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70259-4_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031702587","9783031702594"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70259-4_34","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":"9 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Leipzig","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}