{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T16:04:12Z","timestamp":1774454652453,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-025-04338-x","type":"journal-article","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T07:46:00Z","timestamp":1757403960000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Hybrid Deep Learning Models for Accurate EEG-Based Cognitive State Classification"],"prefix":"10.1007","volume":"6","author":[{"given":"Priya","family":"Nandihal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S. B.","family":"Manoj Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J.","family":"Rajeshwari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. S.","family":"Nagesh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K. L.","family":"Santhosh Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B.","family":"Madhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K. N.","family":"Bharath","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"key":"4338_CR1","unstructured":"Moreno Rocha MA, Miguel N, McCaffery W, Ye J, Lei Y, Creator WZ.), Instrumented Digital and Paper Reading (dataset), 2019. [Online]. Available: https:\/\/risweb.st-Andrews.ac.uk\/portal\/en\/datasets\/instrumented-digital-and-paper-reading-dataset(80f522b6-6d23-4751-9023-21a1e3d0eb5a).html"},{"issue":"1","key":"4338_CR2","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1016\/j.clinph.2019.06.234","volume":"131","author":"C Babiloni","year":"2020","unstructured":"Babiloni C, Barry RJ, Ba\u015far E, Blinowska KJ, Cichocki A, Drinkenburg WH, Hallett M. International federation of clinical neurophysiology (IFCN)\u2013EEG research workgroup: recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: applications in clinical research studies. Clin Neurophysiol. 2020;131(1):285\u2013307.","journal-title":"Clin Neurophysiol"},{"key":"4338_CR3","doi-asserted-by":"crossref","unstructured":"Ha K-W, Jin-Woo J. Motor imagery EEG classification using capsule networks. Sensors 19, no. 13 (2019): 2854.","DOI":"10.3390\/s19132854"},{"issue":"3","key":"4338_CR4","doi-asserted-by":"publisher","first-page":"031001","DOI":"10.1088\/1741-2552\/ab0ab5","volume":"16","author":"A Craik","year":"2019","unstructured":"Craik A, He Y, Contreras-Vidal JL. Deep learning for electroencephalogram (EEG) classification tasks: a review. J Neural Eng. 2019;16(3):031001.","journal-title":"J Neural Eng"},{"key":"4338_CR5","doi-asserted-by":"publisher","first-page":"806","DOI":"10.3389\/fneur.2019.00806","volume":"10","author":"G Ruffini","year":"2019","unstructured":"Ruffini G, Iba\u00f1ez D, Castellano M, Dubreuil-Vall L, Soria-Frisch A, Postuma R. Jean-Fran\u00e7ois gagnon, and Jacques montplaisir. Deep learning with EEG spectrograms in rapid eye movement behavior disorder. Front Neurol. 2019;10:806.","journal-title":"Front Neurol"},{"key":"4338_CR6","doi-asserted-by":"crossref","unstructured":"Mu J, Grayden DB, Tan Y, Oetomo D. Frequency superposition\u2013a multi-frequency stimulation method in SSVEP-based BCIs. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 5924\u20135927. IEEE, 2021.","DOI":"10.1109\/EMBC46164.2021.9630511"},{"issue":"5","key":"4338_CR7","doi-asserted-by":"publisher","first-page":"051001","DOI":"10.1088\/1741-2552\/ab260c","volume":"16","author":"Y Roy","year":"2019","unstructured":"Roy Y, Banville H, Albuquerque I, Gramfort A, Falk TH, Faubert J. Deep learning-based electroencephalography analysis: a systematic review. J Neural Eng. 2019;16(5):051001.","journal-title":"J Neural Eng"},{"key":"4338_CR8","doi-asserted-by":"publisher","first-page":"103243","DOI":"10.1016\/j.bspc.2021.103243","volume":"71","author":"C-T Chang","year":"2022","unstructured":"Chang C-T, Chun-Hui Huang. Novel method of multi-frequency flicker to stimulate SSVEP and frequency recognition. Biomed Signal Process Control. 2022;71:103243.","journal-title":"Biomed Signal Process Control"},{"key":"4338_CR9","doi-asserted-by":"crossref","unstructured":"Luo Y, Zhu L-Z, Bao-Liang, Lu. A GAN-based data augmentation method for multimodal emotion recognition. In International Symposium on Neural Networks, pp. 141\u2013150. Cham: Springer International Publishing, 2019.","DOI":"10.1007\/978-3-030-22796-8_16"},{"key":"4338_CR10","doi-asserted-by":"crossref","unstructured":"Dutta S, Nandy A. Data augmentation for ambulatory EEG based cognitive state taxonomy system with RNN-LSTM. In International Conference on Innovative Techniques and Applications of Artificial Intelligence, pp. 468\u2013473. Cham: Springer International Publishing, 2019.","DOI":"10.1007\/978-3-030-34885-4_38"},{"issue":"4","key":"4338_CR11","first-page":"63","volume":"4","author":"OA Saltykova","year":"2023","unstructured":"Saltykova OA. CNN-PS: electroencephalogram classification of brain States using hybrid machine-deep learning approach. Iraqi J Comput Sci Math. 2023;4(4):63\u201375.","journal-title":"Iraqi J Comput Sci Math"},{"key":"4338_CR12","doi-asserted-by":"crossref","unstructured":"Bunterngchit C, Chearanai T, Bunterngchit Y. Towards Robust Cross-Subject EEG-fNIRS Classification: a hybrid deep learning model with optimized feature selection. In 2024 22nd International Conference on Research and Education in Mechatronics (REM), pp. 291\u2013295. IEEE, 2024.","DOI":"10.1109\/REM63063.2024.10735694"},{"issue":"5","key":"4338_CR13","doi-asserted-by":"publisher","first-page":"2536","DOI":"10.1109\/JBHI.2023.3281793","volume":"28","author":"S Shao","year":"2023","unstructured":"Shao S, Han G, Wang T, Lin C, Song C. Eeg-based mental workload classification method based on hybrid deep learning model under Iot. IEEE J Biomedical Health Inf. 2023;28(5):2536\u201346.","journal-title":"IEEE J Biomedical Health Inf"},{"key":"4338_CR14","doi-asserted-by":"crossref","unstructured":"Chakladar D, Das S, Dey PP, Roy, Iwamura M. EEG-based cognitive state assessment using deep ensemble model and filter bank common spatial pattern. In 2020 25th International Conference on Pattern Recognition (ICPR), pp. 4107\u20134114. IEEE, 2021.","DOI":"10.1109\/ICPR48806.2021.9412869"},{"key":"4338_CR15","doi-asserted-by":"crossref","unstructured":"Wu T, Kong X, Wang Y, Yang X, Liu J, Qi J. Automatic classification of EEG signals via deep learning. In 2021 IEEE 19th International Conference on Industrial Informatics (INDIN), pp. 1\u20136. IEEE, 2021.","DOI":"10.1109\/INDIN45523.2021.9557473"},{"issue":"4","key":"4338_CR16","doi-asserted-by":"publisher","first-page":"1507","DOI":"10.1109\/TCDS.2021.3116079","volume":"14","author":"D Chakladar","year":"2021","unstructured":"Chakladar D, Das PP, Roy, Iwamura M. EEG-based cognitive state classification and analysis of brain dynamics using deep ensemble model and graphical brain network. IEEE Trans Cogn Dev Syst. 2021;14(4):1507\u201319.","journal-title":"IEEE Trans Cogn Dev Syst"},{"key":"4338_CR17","doi-asserted-by":"crossref","unstructured":"Kotwal A, Sharma V, Manhas J. (2023, February). Deep neural based learning of EEG features using spatial, temporal and spectral dimensions across different cognitive workload of human brain: dimensions, methodologies, research challenges and future scope. In International Conference on Emerging Trends in Expert Applications & Security (pp. 61\u201369). Singapore: Springer Nature Singapore.","DOI":"10.1007\/978-981-99-1946-8_7"},{"key":"4338_CR18","doi-asserted-by":"crossref","unstructured":"Zeng H, Yang C, Dai G, Qin F, Zhang J, Kong W. EEG classification of drivermental states by deep learning. Cognit Neurodyn. 2018;12:597\u2013606.","DOI":"10.1007\/s11571-018-9496-y"},{"key":"4338_CR19","doi-asserted-by":"crossref","unstructured":"Lee D-H, Kim S-J, Yeon-Woo C. Classification of distraction levels using hybrid deep neural networks from EEG signals. In 2023 11th International Winter Conference on Brain-Computer Interface (BCI), pp. 1\u20134. IEEE, 2023.","DOI":"10.1109\/BCI57258.2023.10078681"},{"key":"4338_CR20","doi-asserted-by":"publisher","first-page":"110215","DOI":"10.1016\/j.jneumeth.2024.110215","volume":"409","author":"A Kumari","year":"2024","unstructured":"Kumari A, Edla DR, Ravinder Reddy R, Jannu S, Vidyarthi A. Ahmed alkhayyat, and Mirtha Silvana Garat de marin. EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning. J Neurosci Methods. 2024;409:110215.","journal-title":"J Neurosci Methods"},{"issue":"5","key":"4338_CR21","doi-asserted-by":"publisher","first-page":"498","DOI":"10.3390\/brainsci14050498","volume":"14","author":"D Huang","year":"2024","unstructured":"Huang D, Wang Y, Yu LFY, Zhao Z, Zeng P, Wang K, Li N. Decoding subject-driven cognitive States from EEG signals for cognitive brain\u2013computer interface. Brain Sci. 2024;14(5):498.","journal-title":"Brain Sci"},{"key":"4338_CR22","doi-asserted-by":"crossref","unstructured":"Safari MR, Shalbaf R, Bagherzadeh S, Shalbaf A. Classification of mental workload with EEG analysis by using effective connectivity and a hybrid model of CNN and LSTM. Comput Methods Biomech BioMed Eng (2024): 1\u201315.","DOI":"10.1080\/10255842.2024.2386325"},{"key":"4338_CR23","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.jneumeth.2016.10.008","volume":"274","author":"I Sturm","year":"2016","unstructured":"Sturm I, Lapuschkin S, Samek W, Klaus-Robert M\u00fcller. Interpretable deep neural networks for single-trial EEG classification. J Neurosci Methods. 2016;274:141\u20135.","journal-title":"J Neurosci Methods"},{"key":"4338_CR24","doi-asserted-by":"crossref","unstructured":"Dong Y, Xu L, Zheng J, Wu D, Li H, Shao Y, Shi G, Fu W. A hybrid EEG-based stress state classification model using Multi-Domain transfer entropy and pcanet. Brain sciences 14, 6 (2024): 595.","DOI":"10.3390\/brainsci14060595"},{"key":"4338_CR25","unstructured":"Gui Y, Chen MZ, Su Y, Luo G, Yang Y. EEGMamba: bidirectional state space models with mixture of experts for EEG classification. arXiv e-prints (2024): arXiv-2407."},{"key":"4338_CR26","doi-asserted-by":"crossref","unstructured":"Chauhan N, Tomar R, Atri P, Chauhan C, Prakash A, Vikram A. Advancing Brain State Classification through Electroencephalogram (EEG) Analysis: exploring diverse models for improved classification. In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I), vol. 7, pp. 852\u2013857. IEEE, 2024.","DOI":"10.1109\/IC3I61595.2024.10829169"},{"issue":"4","key":"4338_CR27","first-page":"1542","volume":"68","author":"D Hu","year":"2020","unstructured":"Hu D, Cao J, Lai X, Wang Y, Wang S, Ding Y. Epileptic state classification by fusing hand-crafted and deep learning EEG features. IEEE Trans Circuits Syst II Express Briefs. 2020;68(4):1542\u20136.","journal-title":"IEEE Trans Circuits Syst II Express Briefs"},{"key":"4338_CR28","doi-asserted-by":"crossref","unstructured":"Yaobin W. and Dong Chaoyi. Motor imagery EEG signal classification based on spatiotemporal self-attention mechanism and parallel CRNN framework. In 2024 IEEE 6th International Conference on Civil Aviation Safety and Information Technology (ICCASIT), pp. 1135\u20131140. IEEE, 2024.","DOI":"10.1109\/ICCASIT62299.2024.10827924"},{"key":"4338_CR29","doi-asserted-by":"crossref","unstructured":"Amruthamathi A, Devi D. Analysis of EEG signal to classify mental state using stacking ensemble classifier. In AIP Conference Proceedings, vol. 2853, no. 1. AIP Publishing, 2024.","DOI":"10.1063\/5.0197385"},{"key":"4338_CR30","doi-asserted-by":"crossref","unstructured":"Ko W, Jeong S, Song S-K, Heung-Il S. EEG-oriented self-supervised learning with triple information pathways network. IEEE Trans Cybernetics (2024).","DOI":"10.1109\/TCYB.2024.3410844"},{"key":"4338_CR31","doi-asserted-by":"crossref","unstructured":"Kavitha KV, Sudha LR. and J. S. Jayasudha. Optimizing EEG-based emotion recognition with a multi-modal ensemble approach. Results Eng (2025): 104886.","DOI":"10.1016\/j.rineng.2025.104886"},{"key":"4338_CR32","doi-asserted-by":"publisher","first-page":"106770","DOI":"10.1016\/j.bspc.2024.106770","volume":"98","author":"N Panwar","year":"2024","unstructured":"Panwar N, Pandey V. Eeg-cognet: a deep learning framework for cognitive state assessment using Eeg brain connectivity. Biomed Signal Process Control. 2024;98:106770.","journal-title":"Biomed Signal Process Control"},{"key":"4338_CR33","doi-asserted-by":"publisher","first-page":"127271","DOI":"10.1109\/ACCESS.2023.3329678","volume":"11","author":"B Abibullaev","year":"2023","unstructured":"Abibullaev B, Keutayeva A, Zollanvari A,. IEEE Access. 2023;11:127271\u2013301.","journal-title":"IEEE Access"},{"key":"4338_CR34","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1007\/s42452-025-07084-0","volume":"7","author":"S Pichandi","year":"2025","unstructured":"Pichandi S, Balasubramanian G, Chakrapani V, et al. Optimized deep learning models for stress-based stroke prediction from EEG signals. Discov Appl Sci. 2025;7:532. https:\/\/doi.org\/10.1007\/s42452-025-07084-0.","journal-title":"Discov Appl Sci"},{"key":"4338_CR35","doi-asserted-by":"publisher","first-page":"15950","DOI":"10.1038\/s41598-025-00292-z","volume":"15","author":"BR Nayana","year":"2025","unstructured":"Nayana BR, Pavithra MN, Chaitra S, et al. EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models. Sci Rep. 2025;15:15950. https:\/\/doi.org\/10.1038\/s41598-025-00292-z.","journal-title":"Sci Rep"},{"key":"4338_CR36","doi-asserted-by":"publisher","first-page":"106182","DOI":"10.1016\/j.bspc.2024.106182","volume":"93","author":"M Ying","year":"2024","unstructured":"Ying M, Shao X, Zhu J, Zhao Q, Li X. EDT: an EEG-based attention model for feature learning and depression recognition. Biomed Signal Process Control. 2024;93:106182.","journal-title":"Biomed Signal Process Control"},{"key":"4338_CR37","doi-asserted-by":"crossref","unstructured":"Wang D, Shi J, Liu M, Han W, Bi L. and Weijie Fei. Brain-Inspired deep learning model for EEG-based low-quality video target detection with phased encoding and aligned fusion. Expert Syst Appl (2025): 128189.","DOI":"10.1016\/j.eswa.2025.128189"},{"key":"4338_CR38","doi-asserted-by":"publisher","first-page":"10935","DOI":"10.1038\/s41598-025-86294-3","volume":"15","author":"T Klein","year":"2025","unstructured":"Klein T, Minakowski P, Sager S. Flexible patched brain transformer model for EEG decoding. Sci Rep. 2025;15:10935. https:\/\/doi.org\/10.1038\/s41598-025-86294-3.","journal-title":"Sci Rep"},{"key":"4338_CR39","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/s42979-025-03743-6","volume":"6","author":"MK Titkanlou","year":"2025","unstructured":"Titkanlou MK, Pham DT, Mou\u010dek R. Classification of EEG signal using deep learning architectures based motor-imagery for an upper-limb rehabilitation exoskeleton. SN COMPUT SCI. 2025;6:193. https:\/\/doi.org\/10.1007\/s42979-025-03743-6","journal-title":"SN COMPUT SCI"},{"key":"4338_CR40","doi-asserted-by":"publisher","unstructured":"Xia M, Zhang Y, Wu Y, Wang X. An end-to-end deep learning model for EEG-Based major depressive disorder classification, in IEEE access, 11, pp. 41337\u201347, 2023, https:\/\/doi.org\/10.1109\/ACCESS.2023.3270426","DOI":"10.1109\/ACCESS.2023.3270426"},{"issue":"6","key":"4338_CR41","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1007\/s42979-025-04148-1","volume":"6","author":"SA Karthik","year":"2025","unstructured":"Karthik SA, Bharath KN, Ramji BR, Puttegowda K. Aruna, and DS Sunil kumar. Enhanced EEG signal processing for accurate epileptic seizure detection. SN Comput Sci. 2025;6(6):608.","journal-title":"SN Comput Sci"},{"issue":"6","key":"4338_CR42","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1007\/s42979-025-04128-5","volume":"6","author":"KV Sudheesh","year":"2025","unstructured":"Sudheesh KV, Puttegowda K, Naveenkumar HN, Chethan K. Convolution neural network-based alzheimer disease detection system using medical image retrieval approach with Multi-Class classification. SN Comput Sci. 2025;6(6):587.","journal-title":"SN Comput Sci"},{"key":"4338_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2174\/0118749445286599240311102956","volume":"17","author":"K Puttegowda","year":"2024","unstructured":"Puttegowda K, Sunil Kumar DS, Sahana Mallu, Vijay CP, Vinayakumar Ravi, Sushmitha BC. Automatic COVID-19 prediction with comprehensible machine learning models. Open Public Health J. 2024;17:1.","journal-title":"Open Public Health J"},{"key":"4338_CR44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2174\/18750362-v16-231005-2023-5","volume":"16","author":"KV Sudheesh","year":"2023","unstructured":"Sudheesh KV, Ravi V, Almeshari M, Alzamil Y, Sunil Kumar DS. A new deep learning model based on neuroimaging for predicting alzheimer\u2019s disease. Open Bioinf J. 2023;16:1.","journal-title":"Open Bioinf J"},{"key":"4338_CR45","doi-asserted-by":"crossref","unstructured":"Parameshachari B, Sunil Kumar DS, Sudheesh KV, Deepak R, Deepak HA. Classification of Alzheimer\u2019s Disease Using 2D\/3D Convolutional Neural Networks. In 2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC), pp. 1\u20135. IEEE, 2023.","DOI":"10.1109\/ICAISC58445.2023.10200305"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04338-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-04338-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04338-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T07:46:17Z","timestamp":1757403977000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-04338-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,8]]},"references-count":45,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["4338"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-04338-x","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,8]]},"assertion":[{"value":"24 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research Involving Human and \/or Animals"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"806"}}