{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:19:14Z","timestamp":1767323954228,"version":"3.48.0"},"publisher-location":"Singapore","reference-count":47,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819556984","type":"print"},{"value":"9789819556991","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-5699-1_20","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:14:40Z","timestamp":1767323680000},"page":"287-302","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Geometry-Aware Self-attention Network with\u00a0Adaptive Log-Euclidean Metric for\u00a0EEG Decoding"],"prefix":"10.1007","author":[{"given":"Zihao","family":"Bi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoning","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao-Jun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"20_CR1","doi-asserted-by":"crossref","unstructured":"Altuwaijri, G.A., Muhammad, G., Altaheri, H., Alsulaiman, M.: A multi-branch convolutional neural network with squeeze-and-excitation attention blocks for eeg-based motor imagery signals classification. Diagnostics, 995 (2022)","DOI":"10.3390\/diagnostics12040995"},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Fast and Simple Computations on Tensors with Log-Euclidean Metrics. Ph.D. thesis, Inria (2005)","DOI":"10.1007\/11566465_15"},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Geometric means in a novel vector space structure on symmetric positive-definite matrices. SIAM J. Matrix Anal. Appl., 328\u2013347 (2007)","DOI":"10.1137\/050637996"},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Berger, M.: A panoramic view of Riemannian geometry (2003)","DOI":"10.1007\/978-3-642-18245-7"},{"key":"20_CR5","unstructured":"Brooks, D., Schwander, O., Barbaresco, F., Schneider, J.Y., Cord, M.: Riemannian batch normalization for SPD neural networks. NeurIPS, 15463\u201315474 (2019)"},{"key":"20_CR6","unstructured":"Brunner, C., Leeb, R., M\u00fcller-Putz, G., Schl\u00f6gl, A., Pfurtscheller, G.: BCI competition 2008\u2013graz data set a. Institute for knowledge discovery (laboratory of brain-computer interfaces), Graz University of Technology p.\u00a034 (2008)"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Z., Song, Y., Liu, G., Kompella, R.R., Wu, X.J., Sebe, N.: Riemannian multinomial logistics regression for SPD neural networks. In: CVPR, pp. 17086\u201317096 (2024)","DOI":"10.1109\/CVPR52733.2024.01617"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Chen, Z., Song, Y., Xu, T., Huang, Z., Wu, X.J., Sebe, N.: Adaptive log-euclidean metrics for SPD matrix learning. IEEE Trans. Image Process., 5194\u20135205 (2024)","DOI":"10.1109\/TIP.2024.3451930"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Garcia-Hernando, G., Yuan, S., Baek, S., Kim, T.K.: First-person hand action benchmark with RGB-D videos and 3D hand pose annotations. In: CVPR, pp. 409\u2013419 (2018)","DOI":"10.1109\/CVPR.2018.00050"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Han, C., Xu, G., Xie, J., Chen, C., Zhang, S.: Highly interactive brain\u2013computer interface based on flicker-free steady-state motion visual evoked potential. Sci. Rep., 5835 (2018)","DOI":"10.1038\/s41598-018-24008-8"},{"key":"20_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Herrmann, C.S.: Human eeg responses to 1\u2013100 hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena. Exp. Brain Res., 346\u2013353 (2001)","DOI":"10.1007\/s002210100682"},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Hine, G.E., Maiorana, E., Campisi, P.: Resting-state eeg: a study on its non-stationarity for biometric applications. In: BIOSIG, pp.\u00a01\u20135 (2017)","DOI":"10.23919\/BIOSIG.2017.8053519"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Hu, C., et al.: A correlation manifold self-attention network for eeg decoding. In: IJCAI (2025)","DOI":"10.24963\/ijcai.2025\/598"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Huang, Z., Van\u00a0Gool, L.: A Riemannian network for SPD matrix learning. In: AAAI, pp. 2036\u20132042 (2017)","DOI":"10.1609\/aaai.v31i1.10866"},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Ingolfsson, T.M., Hersche, M., Wang, X., Kobayashi, N., Cavigelli, L., Benini, L.: EEG-TCNet: an accurate temporal convolutional network for embedded motor-imagery brain-machine interfaces. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 2958\u20132965 (2020)","DOI":"10.1109\/SMC42975.2020.9283028"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Iturrate, I., Antelis, J., Minguez, J.: Synchronous eeg brain-actuated wheelchair with automated navigation. In: IEEE International Conference on Robotics Automation, pp. 2318\u20132325 (2009)","DOI":"10.1109\/ROBOT.2009.5152580"},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Johnson, D.H.: Signal-to-noise ratio. Scholarpedia, p.\u00a02088 (2006)","DOI":"10.4249\/scholarpedia.2088"},{"key":"20_CR19","doi-asserted-by":"crossref","unstructured":"Ke, S., Yang, B., Qin, Y., Rong, F., Zhang, J., Zheng, Y.: FACT-Net: a frequency adapter cnn with temporal-periodicity inception for fast and accurate mi-eeg decoding. IEEE Trans. Neural Syst. Rehabil. Eng., 4131\u20134142 (2024)","DOI":"10.1109\/TNSRE.2024.3499998"},{"key":"20_CR20","doi-asserted-by":"crossref","unstructured":"Khare, S.K., Bajaj, V., Acharya, U.R.: SPWVD-CNN for automated detection of schizophrenia patients using eeg signals. IEEE Trans. Instrum. Meas., 1\u20139 (2021)","DOI":"10.1109\/TIM.2021.3070608"},{"key":"20_CR21","doi-asserted-by":"crossref","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\u2013computer interfaces. J. Neural Eng., 056013 (2018)","DOI":"10.1088\/1741-2552\/aace8c"},{"key":"20_CR22","doi-asserted-by":"crossref","unstructured":"Lebanon, G., Lafferty, J.: Hyperplane margin classifiers on the multinomial manifold. In: ICML, p.\u00a066 (2004)","DOI":"10.1145\/1015330.1015333"},{"key":"20_CR23","unstructured":"Mane, R., et al.: FBCNet: a multi-view convolutional neural network for brain-computer interface. arXiv preprint arXiv:2104.01233 (2021)"},{"key":"20_CR24","unstructured":"M\u00fcller, M., R\u00f6der, T., Clausen, M., Eberhardt, B., Kr\u00fcger, B., Weber, A.: Mocap database hdm05. Institut f\u00fcr Informatik II, Universit\u00e4t Bonn 2(7) (2007)"},{"key":"20_CR25","doi-asserted-by":"crossref","unstructured":"Musallam, Y.K., et al.: Electroencephalography-based motor imagery classification using temporal convolutional network fusion. Biomed. Signal Process. Control, 102826 (2021)","DOI":"10.1016\/j.bspc.2021.102826"},{"key":"20_CR26","unstructured":"Nikolopoulos, S.: MAMEM EEG SSVEP dataset II (256 channels 11 subjects 5 frequencies presented simultaneously) (2021)"},{"key":"20_CR27","unstructured":"Pan, Y.T., Chou, J.L., Wei, C.S.: MAtt: a manifold attention network for eeg decoding. NeurIPS, 31116\u201331129 (2022)"},{"key":"20_CR28","doi-asserted-by":"crossref","unstructured":"Pennec, X., Fillard, P., Ayache, N.: A Riemannian framework for tensor computing. IJCV, 41\u201366 (2006)","DOI":"10.1007\/s11263-005-3222-z"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Perrin, M., Maby, E., Daligault, S., Bertrand, O., Mattout, J.: Objective and subjective evaluation of online error correction during p300-based spelling. Adv. Hum. Comput. Interact., 578295 (2012)","DOI":"10.1155\/2012\/578295"},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"Rajwal, S., Aggarwal, S.: Convolutional neural network-based eeg signal analysis: a systematic review. Arch. Comput. Methods Eng., 3585\u20133615 (2023)","DOI":"10.1007\/s11831-023-09920-1"},{"issue":"11","key":"20_CR31","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."},{"key":"20_CR32","doi-asserted-by":"crossref","unstructured":"Subha, D.P., Joseph, P.K., Acharya\u00a0U, R., Lim, C.M.: Eeg signal analysis: a survey. J. Med. Syst., 195\u2013212 (2010)","DOI":"10.1007\/s10916-008-9231-z"},{"key":"20_CR33","doi-asserted-by":"crossref","unstructured":"Suh, Y.J., Kim, B.H.: Riemannian embedding banks for common spatial patterns with eeg-based SPD neural networks. In: AAAI, pp. 854\u2013862 (2021)","DOI":"10.1609\/aaai.v35i1.16168"},{"key":"20_CR34","unstructured":"Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: ICML, pp. 6105\u20136114 (2019)"},{"key":"20_CR35","doi-asserted-by":"crossref","unstructured":"Thanwerdas, Y., Pennec, X.: O (n)-invariant Riemannian metrics on SPD matrices. Linear Algebra Appl., 163\u2013201 (2023)","DOI":"10.1016\/j.laa.2022.12.009"},{"key":"20_CR36","unstructured":"Tibermacine, I.E., Russo, S., Tibermacine, A., Rabehi, A., Nail, B., Kadri, K., Napoli, C.: Riemannian geometry-based eeg approaches: A literature review. arXiv preprint arXiv:2407.20250 (2024)"},{"key":"20_CR37","doi-asserted-by":"crossref","unstructured":"Tu, L.W.: Manifolds. In: An Introduction to Manifolds, pp. 47\u201383. Springer (2011)","DOI":"10.1007\/978-1-4419-7400-6_3"},{"key":"20_CR38","unstructured":"Wang, R., Hu, C., Chen, Z., Wu, X.J., Song, X.: A Grassmannian manifold self-attention network for signal classification. In: IJCAI, pp. 5099\u20135107 (2024)"},{"key":"20_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2025.3643065","volume":"74","author":"R Wang","year":"2025","unstructured":"Wang, R., Jin, J., Chen, Z., Wu, C., Wu, X.J., Sebe, N.: Structural topology refinement network for skeleton-based action recognition. IEEE Trans. Instrum. Meas. 74, 1\u201316 (2025)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"7","key":"20_CR40","doi-asserted-by":"publisher","first-page":"8924","DOI":"10.1109\/TNNLS.2022.3216811","volume":"35","author":"R Wang","year":"2024","unstructured":"Wang, R., Wu, X.J., Chen, Z., Hu, C., Kittler, J.: SPD manifold deep metric learning for image set classification. IEEE Trans. Neural Networks Learn. Syst. 35(7), 8924\u20138938 (2024)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"20_CR41","doi-asserted-by":"crossref","unstructured":"Wang, R., Wu, X.J., Chen, Z., Xu, T., Kittler, J.: Dreamnet: a deep Riemannian manifold network for SPD matrix learning. In: ACCV, pp. 3241\u20133257 (2022)","DOI":"10.1007\/978-3-031-26351-4_39"},{"key":"20_CR42","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.neunet.2022.11.030","volume":"161","author":"R Wang","year":"2023","unstructured":"Wang, R., Wu, X.J., Xu, T., Hu, C., Kittler, J.: U-SPDNet: an SPD manifold learning-based neural network for visual classification. Neural Netw. 161, 382\u2013396 (2023)","journal-title":"Neural Netw."},{"key":"20_CR43","doi-asserted-by":"crossref","unstructured":"Wei, C.S., Koike-Akino, T., Wang, Y.: Spatial component-wise convolutional network (SCCNet) for motor-imagery eeg classification. In: EEE\/EMBS International Conference on Neural Engineering, pp. 328\u2013331 (2019)","DOI":"10.1109\/NER.2019.8716937"},{"key":"20_CR44","doi-asserted-by":"crossref","unstructured":"Yin, W., Liang, Z., Zhang, J., Liu, Q.: Partial least square regression via three-factor svd-type manifold optimization for eeg decoding. In: PRCV, pp. 778\u2013787 (2022)","DOI":"10.1007\/978-3-031-18907-4_60"},{"key":"20_CR45","unstructured":"Zhang, G., Etemad, A.: Rfnet: Riemannian fusion network for eeg-based brain-computer interfaces. arXiv preprint arXiv:2008.08633 (2020)"},{"key":"20_CR46","doi-asserted-by":"crossref","unstructured":"Zhang, J., Guo, W., Yu, H., Wang, Y.: Motor imagery eeg recognition based on an improved convolutional neural network with parallel gate recurrent unit. In: PRCV, pp. 316\u2013327 (2023)","DOI":"10.1007\/978-981-99-8543-2_26"},{"key":"20_CR47","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Luo, T.j., Zhang, X., Han, T.: Spatial feature regularization and label decoupling based cross-subject motor imagery eeg decoding. In: PRCV, pp. 407\u2013423 (2023)","DOI":"10.1007\/978-981-99-8558-6_34"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5699-1_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:14:43Z","timestamp":1767323683000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5699-1_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819556984","9789819556991"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5699-1_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","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":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}