{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T07:23:14Z","timestamp":1767943394008,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819543779","type":"print"},{"value":"9789819543786","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T00:00:00Z","timestamp":1762646400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T00:00:00Z","timestamp":1762646400000},"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\/978-981-95-4378-6_44","type":"book-chapter","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T15:52:57Z","timestamp":1762789977000},"page":"625-638","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Meta-Adaptive Hilbert Framework for\u00a0Continual Cross-Subject Neural Decoding in\u00a0BCIs"],"prefix":"10.1007","author":[{"given":"Deepak","family":"Mewada","sequence":"first","affiliation":[]},{"given":"Shamin","family":"Aggarwal","sequence":"additional","affiliation":[]},{"given":"Monalisa","family":"Sharma","sequence":"additional","affiliation":[]},{"given":"Debasis","family":"Samanta","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,9]]},"reference":[{"key":"44_CR1","doi-asserted-by":"crossref","unstructured":"Lotte, F., et al.: A review of classification algorithms for EEG-based BCIs: a 10-year update. J. Neural Eng. 15(3) (2018)","DOI":"10.1088\/1741-2552\/aab2f2"},{"key":"44_CR2","doi-asserted-by":"crossref","unstructured":"Craik, A., He, Y., Contreras-Vidal, J.L.: Deep learning for electroencephalogram (EEG) classification tasks: a review. J. Neural Eng. 16(3) (2019)","DOI":"10.1088\/1741-2552\/ab0ab5"},{"key":"44_CR3","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.neucom.2020.04.157","volume":"444","author":"X Zhang","year":"2021","unstructured":"Zhang, X., et al.: Survey of deep learning methods for EEG decoding. Neurocomputing 444, 92\u2013115 (2021)","journal-title":"Neurocomputing"},{"key":"44_CR4","doi-asserted-by":"crossref","unstructured":"Ang, K.K., Chin, Z.Y., Zhang, H., Guan, C.: Filter bank common spatial pattern (FBCSP) in brain-computer interface. In Proc. IEEE International Joint Conference on Neural Networks, 2390\u20132397 (2008)","DOI":"10.1109\/IJCNN.2008.4634130"},{"key":"44_CR5","doi-asserted-by":"crossref","unstructured":"Roy, Y., et al.: Deep learning-based electroencephalography analysis: a systematic review. J. Neural Eng. 16(5) (2019)","DOI":"10.1088\/1741-2552\/ab260c"},{"issue":"4","key":"44_CR6","doi-asserted-by":"publisher","first-page":"920","DOI":"10.1109\/TBME.2011.2172210","volume":"59","author":"A Barachant","year":"2012","unstructured":"Barachant, A., Bonnet, S., Congedo, M., Jutten, C.: Multiclass brain\u2013computer interface classification by Riemannian geometry. IEEE Trans. Biomed. Eng. 59(4), 920\u2013928 (2012)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"1","key":"44_CR7","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/MSP.2008.4408441","volume":"25","author":"B Blankertz","year":"2008","unstructured":"Blankertz, B., et al.: Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process. Mag. 25(1), 41\u201356 (2008)","journal-title":"IEEE Signal Process. Mag."},{"issue":"11","key":"44_CR8","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."},{"issue":"5","key":"44_CR9","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aace8c","volume":"15","author":"VJ Lawhern","year":"2018","unstructured":"Lawhern, V.J., 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."},{"issue":"11","key":"44_CR10","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1109\/TNSRE.2016.2544108","volume":"24","author":"D Wu","year":"2016","unstructured":"Wu, D., Lawhern, V.J., Hairston, W.D., Lance, B.: Switching EEG headsets made easy: Reducing offline calibration effort using active weighted adaptation regularization. IEEE Trans. Neural Syst. Rehabil. Eng. 24(11), 1125\u20131137 (2016)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"1","key":"44_CR11","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/MCI.2015.2501545","volume":"11","author":"V Jayaram","year":"2016","unstructured":"Jayaram, V., et al.: Transfer learning in brain-computer interfaces. IEEE Comput. Intell. Mag. 11(1), 20\u201331 (2016)","journal-title":"IEEE Comput. Intell. Mag."},{"issue":"1","key":"44_CR12","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1109\/TCDS.2018.2826840","volume":"11","author":"Z Lan","year":"2019","unstructured":"Lan, Z., Sourina, O., Wang, L., Scherer, R., M\u00fcller-Putz, G.: Domain adaptation techniques for EEG-based emotion recognition: a comparative study on two public datasets. IEEE Trans. Cogn. Dev. Syst. 11(1), 85\u201394 (2019)","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"44_CR13","doi-asserted-by":"crossref","unstructured":"Xu, Y., et al.: Selective cross-subject transfer learning based on Riemannian tangent space for motor imagery brain\u2013computer interface. Front. Neurosci. 15 (2021)","DOI":"10.3389\/fnins.2021.779231"},{"key":"44_CR14","unstructured":"Li, D., et al.: Personalized continual EEG decoding: Retaining and transferring knowledge"},{"issue":"2","key":"44_CR15","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abda0b","volume":"18","author":"SM Peterson","year":"2021","unstructured":"Peterson, S.M., et al.: Generalized neural decoders for transfer learning across participants and recording modalities. J. Neural Eng. 18(2), 026008 (2021)","journal-title":"J. Neural Eng."},{"key":"44_CR16","unstructured":"Mane, R., et al.: FBCNet: a multi-view convolutional neural network for brain-computer interface. IEEE Trans. Neural Syst. Rehabilitation Eng. (to appear) (2021)"},{"issue":"5","key":"44_CR17","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1109\/TBME.2017.2742541","volume":"65","author":"P Zanini","year":"2018","unstructured":"Zanini, P., et al.: Transfer learning: a riemannian geometry framework with applications to brain\u2013computer interfaces. IEEE Trans. Biomed. Eng. 65(5), 1107\u20131116 (2018)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"44_CR18","doi-asserted-by":"crossref","unstructured":"Tan, C., Sun, F., Zhang, W., Zhang, D.: Deep transfer learning for EEG-based brain computer interface. In: Proc. IEEE ICASSP, 916\u2013920 (2018)","DOI":"10.1109\/ICASSP.2018.8462115"},{"key":"44_CR19","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proc. ICML, 1126\u20131135 (2017)."},{"key":"44_CR20","unstructured":"Berdyshev, D.A., Grachev, A.M., Shishkin, S.L., Kozyrskiy, B.L.: EEG-Reptile: an automatized reptile-based meta-learning library for BCIs. arXiv preprint arXiv:2412.19725 (2025)"},{"key":"44_CR21","doi-asserted-by":"crossref","unstructured":"Duan, T., et al.: Ultra efficient transfer learning with meta update for cross subject EEG classification. arXiv preprint arXiv:2003.06113 (2020)","DOI":"10.21428\/594757db.6bc1ca44"},{"issue":"13","key":"44_CR22","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1073\/pnas.1611835114","volume":"114","author":"J Kirkpatrick","year":"2017","unstructured":"Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. Proc. Natl. Acad. Sci. 114(13), 3521\u20133526 (2017)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"44_CR23","doi-asserted-by":"crossref","unstructured":"Li, Z., Hoiem, D.: Learning without forgetting. In: Proc. ECCV, 614\u2013629 (2017).","DOI":"10.1007\/978-3-319-46493-0_37"},{"key":"44_CR24","doi-asserted-by":"crossref","unstructured":"Banluesombatkul, N., et al.: MetaSleepLearner: a pilot study on fast adaptation of bio-signals-based sleep stage classifier to new individual subject using meta-learning. arXiv preprint arXiv:2004.04157 (2020).","DOI":"10.1109\/JBHI.2020.3037693"},{"key":"44_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, D., Yao, L., Zhang, X., et al.: Cascade and parallel convolutional recurrent neural networks on EEG-based intention recognition for brain-computer interface. In: Proc. AAAI, pp. 6584\u20136591 (2018).","DOI":"10.1609\/aaai.v32i1.11496"},{"key":"44_CR26","doi-asserted-by":"crossref","unstructured":"Kaplanoglu, E., et al.: Decoding brain\u2019s electrical activity: leveraging Hilbert transform techniques for EEG analysis. COJ Electron. Commun. 3(1) (2024)","DOI":"10.31031\/COJEC.2024.04.000552"},{"key":"44_CR27","doi-asserted-by":"crossref","unstructured":"Li, D., et al.: Model-agnostic meta-learning for EEG motor imagery decoding in brain\u2013computer interfacing. IEEE NER 2021. arXiv preprint arXiv:2411.11874 (2024)","DOI":"10.1109\/NER49283.2021.9441077"},{"key":"44_CR28","doi-asserted-by":"crossref","unstructured":"Liu, S., et al.: Subject adaptation convolutional neural network for EEG-based motor imagery classification. J. Neural Eng. 19(6) (2022)","DOI":"10.1088\/1741-2552\/ac9c94"},{"key":"44_CR29","doi-asserted-by":"publisher","unstructured":"Sun, Q., Liu, Y., Chua, T.-S., Schiele, B.: Meta-transfer learning for few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 403\u2013412 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00049","DOI":"10.1109\/CVPR.2019.00049"},{"key":"44_CR30","unstructured":"Brunner, C., Leeb, R., M\u00fcller-Putz, G.R., Schl\u00f6gl, A., Pfurtscheller, G.: BCI Competition 2008 \u2013 Graz data set A. Graz University of Technology, Institute for Knowledge Discovery (Laboratory of Brain-Computer Interfaces) (2008)"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4378-6_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T05:59:21Z","timestamp":1767938361000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4378-6_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,9]]},"ISBN":["9789819543779","9789819543786"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4378-6_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,9]]},"assertion":[{"value":"9 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Okinawa","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"20 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2025.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}