{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T14:00:32Z","timestamp":1793800832827,"version":"4.6.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T00:00:00Z","timestamp":1743638400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T00:00:00Z","timestamp":1743638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-96052-0","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T16:21:33Z","timestamp":1743783693000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion"],"prefix":"10.1038","volume":"15","author":[{"given":"Kavita","family":"Rawat","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Trapti","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,3]]},"reference":[{"key":"96052_CR1","doi-asserted-by":"publisher","unstructured":"Rahaman, M. A., Chen, J., Fu, Z., Lewis, N., Iraji, A. & Calhoun, V. D. Multi-modal deep learning of functional and structural neuroimaging and genomic data to predict mental illness. In 2021 43rd annual international conference of the IEEE engineering in medicine & biology society (EMBC), Mexico, pp. 3267\u20133272 (2021). https:\/\/doi.org\/10.1109\/EMBC46164.2021.9630693.","DOI":"10.1109\/EMBC46164.2021.9630693"},{"key":"96052_CR2","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Lin, W., Liu, M. & Mahmoud, M. Multimodal deep learning framework for mental disorder recognition. In 2020 15th IEEE international conference on automatic face and gesture recognition (FG 2020), Buenos Aires, Argentina, pp. 344\u2013350 (2020). https:\/\/doi.org\/10.1109\/FG47880.2020.00033.","DOI":"10.1109\/FG47880.2020.00033"},{"key":"96052_CR3","doi-asserted-by":"publisher","DOI":"10.3389\/fcomp.2022.780580","volume":"4","author":"L-M Vortmann","year":"2022","unstructured":"Vortmann, L.-M., Ceh, S. & Putze, F. Multimodal EEG and eye tracking feature fusion approaches for attention classification in hybrid BCIs. Front. Comput. Sci. 4, 780580. https:\/\/doi.org\/10.3389\/fcomp.2022.780580 (2022).","journal-title":"Front. Comput. Sci."},{"key":"96052_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.health.2024.100350","volume":"6","author":"Md Islam","year":"2024","unstructured":"Islam, Md., Hassan, S., Akter, S., Jibon, F. & Sahidullah, Md. A comprehensive review of predictive analytics models for mental illness using machine learning algorithms. Healthcare Anal. 6, 100350. https:\/\/doi.org\/10.1016\/j.health.2024.100350 (2024).","journal-title":"Healthcare Anal."},{"issue":"4","key":"96052_CR5","doi-asserted-by":"publisher","first-page":"493","DOI":"10.18502\/ijps.v18i4.13637","volume":"18","author":"RM Hern\u00e1ndez","year":"2023","unstructured":"Hern\u00e1ndez, R. M. et al. Brain complexity and psychiatric disorders. Iran J. Psychiatry. 18(4), 493\u2013502. https:\/\/doi.org\/10.18502\/ijps.v18i4.13637 (2023).","journal-title":"Iran J. Psychiatry."},{"issue":"1","key":"96052_CR6","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3390\/diagnostics13010002","volume":"13","author":"WJ Yan","year":"2022","unstructured":"Yan, W. J., Ruan, Q. N. & Jiang, K. Challenges for artificial intelligence in recognizing mental disorders. Diagnostics (Basel) 13(1), 2. https:\/\/doi.org\/10.3390\/diagnostics13010002 (2022).","journal-title":"Diagnostics (Basel)"},{"key":"96052_CR7","doi-asserted-by":"publisher","first-page":"619","DOI":"10.12688\/f1000research.10300.1","volume":"6","author":"T Frodl","year":"2017","unstructured":"Frodl, T. Recent advances in predicting responses to antidepressant treatment. F1000Res 6, 619. https:\/\/doi.org\/10.12688\/f1000research.10300.1 (2017).","journal-title":"F1000Res"},{"key":"96052_CR8","doi-asserted-by":"publisher","unstructured":"Abrol, A., Fu, Z., Du, Y. & Calhoun, V. D. Multimodal data fusion of deep learning and dynamic functional connectivity features to predict Alzheimer\u2019s disease progression. In 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC), Berlin, Germany, pp. 4409\u20134413 (2019). https:\/\/doi.org\/10.1109\/EMBC.2019.8856500.","DOI":"10.1109\/EMBC.2019.8856500"},{"issue":"4","key":"96052_CR9","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1109\/TCBB.2023.3252577","volume":"21","author":"H Ke","year":"2024","unstructured":"Ke, H. et al. Deep factor learning for accurate brain neuroimaging data analysis on discrimination for structural MRI and functional MRI. IEEE\/ACM Trans. Comput. Biol. Bioinform. 21(4), 582\u2013595. https:\/\/doi.org\/10.1109\/TCBB.2023.3252577 (2024).","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"issue":"14","key":"96052_CR10","doi-asserted-by":"publisher","first-page":"225","DOI":"10.3389\/fnins.2020.00225","volume":"31","author":"F Sanvito","year":"2020","unstructured":"Sanvito, F. et al. fMRI-targeted high-angular resolution diffusion MR tractography to identify functional language tracts in healthy controls and glioma patients. Front. Neurosci. 31(14), 225. https:\/\/doi.org\/10.3389\/fnins.2020.00225 (2020).","journal-title":"Front. Neurosci."},{"key":"96052_CR11","doi-asserted-by":"publisher","first-page":"3254","DOI":"10.1038\/s41598-020-74399-w","volume":"11","author":"J Venugopalan","year":"2021","unstructured":"Venugopalan, J. et al. Multimodal deep learning models for early detection of Alzheimer\u2019s disease stage. Sci. Rep. 11, 3254. https:\/\/doi.org\/10.1038\/s41598-020-74399-w (2021).","journal-title":"Sci. Rep."},{"key":"96052_CR12","doi-asserted-by":"publisher","first-page":"79419","DOI":"10.1109\/ACCESS.2022.3194145","volume":"10","author":"H Alsolai","year":"2022","unstructured":"Alsolai, H. et al. A systematic review of literature on automated sleep scoring. IEEE Access 10, 79419\u201379443. https:\/\/doi.org\/10.1109\/ACCESS.2022.3194145 (2022).","journal-title":"IEEE Access"},{"key":"96052_CR13","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1038\/s41746-022-00712-8","volume":"5","author":"A Kline","year":"2022","unstructured":"Kline, A. et al. Multimodal machine learning in precision health: A scoping review. Npj Digit. Med. 5, 171. https:\/\/doi.org\/10.1038\/s41746-022-00712-8 (2022).","journal-title":"Npj Digit. Med."},{"issue":"4","key":"96052_CR14","doi-asserted-by":"publisher","first-page":"605","DOI":"10.3390\/electronics11040605","volume":"11","author":"O Ouichka","year":"2022","unstructured":"Ouichka, O., Echtioui, A. & Hamam, H. Deep learning models for predicting epileptic seizures using iEEG signals. Electronics 11(4), 605. https:\/\/doi.org\/10.3390\/electronics11040605 (2022).","journal-title":"Electronics"},{"key":"96052_CR15","doi-asserted-by":"publisher","first-page":"848","DOI":"10.3389\/fnhum.2013.00848","volume":"7","author":"M Ahn","year":"2013","unstructured":"Ahn, M. et al. Gamma band activity associated with BCI performance: Simultaneous MEG\/EEG study. Front. Hum. Neurosci. 7, 848 (2013).","journal-title":"Front. Hum. Neurosci."},{"issue":"2022","key":"96052_CR16","doi-asserted-by":"publisher","first-page":"7751263","DOI":"10.1155\/2022\/7751263","volume":"20","author":"M Natu","year":"2022","unstructured":"Natu, M., Bachute, M., Gite, S., Kotecha, K. & Vidyarthi, A. Review on epileptic seizure prediction: Machine learning and deep learning approaches. Comput. Math. Methods Med. 20(2022), 7751263. https:\/\/doi.org\/10.1155\/2022\/7751263 (2022).","journal-title":"Comput. Math. Methods Med."},{"issue":"1","key":"96052_CR17","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1002\/hbm.23834","volume":"39","author":"S Ahn","year":"2017","unstructured":"Ahn, S. et al. Interbrain phase synchronization during tum-taking verbal interaction\u2013a hyperscanning study using simultaneous EEG\/MEG. Hum. Brain Mapp. 39(1), 171\u2013188 (2017).","journal-title":"Hum. Brain Mapp."},{"key":"96052_CR18","doi-asserted-by":"publisher","unstructured":"Wongkoblap, A., Vadillo, M. A. & Curcin, V. Detecting and treating mental illness on social networks. In 2017 IEEE international conference on healthcare informatics (ICHI), Park City, UT, USA, pp. 330\u2013330 (2017). https:\/\/doi.org\/10.1109\/ICHI.2017.24.","DOI":"10.1109\/ICHI.2017.24"},{"key":"96052_CR19","doi-asserted-by":"publisher","unstructured":"Zebin, T., Peek, N. & Casson, A. J. Physical activity based classification of serious mental illness group participants in the UK Biobank using ensemble dense neural networks. In 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC), Berlin, Germany, pp. 1251-1254 (2019). https:\/\/doi.org\/10.1109\/EMBC.2019.8857532.","DOI":"10.1109\/EMBC.2019.8857532"},{"issue":"21","key":"96052_CR20","doi-asserted-by":"publisher","first-page":"21232","DOI":"10.1109\/JSEN.2022.3208427","volume":"22","author":"Z-H Wang","year":"2022","unstructured":"Wang, Z.-H. & Wu, Y.-C. A novel rapid assessment of mental stress by using PPG signals based on deep learning. IEEE Sens. J. 22(21), 21232\u201321239. https:\/\/doi.org\/10.1109\/JSEN.2022.3208427 (2022).","journal-title":"IEEE Sens. J."},{"key":"96052_CR21","doi-asserted-by":"publisher","first-page":"102444","DOI":"10.1016\/j.media.2022.102444","volume":"79","author":"X Chen","year":"2022","unstructured":"Chen, X. et al. Recent advances and clinical applications of deep learning in medical image analysis. Med. Image Anal. 79, 102444. https:\/\/doi.org\/10.1016\/j.media.2022.102444 (2022).","journal-title":"Med. Image Anal."},{"key":"96052_CR22","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1007\/s40846-022-00758-9","volume":"42","author":"M\u00c1 Luj\u00e1n","year":"2022","unstructured":"Luj\u00e1n, M. \u00c1. et al. Mental disorder diagnosis from EEG signals employing automated leaning procedures based on radial basis functions. J. Med. Biol. Eng. 42, 853\u2013859. https:\/\/doi.org\/10.1007\/s40846-022-00758-9 (2022).","journal-title":"J. Med. Biol. Eng."},{"issue":"18","key":"96052_CR23","doi-asserted-by":"publisher","first-page":"1347082","DOI":"10.3389\/fnhum.2024.1347082","volume":"14","author":"J Rahul","year":"2024","unstructured":"Rahul, J., Sharma, D., Sharma, L. D., Nanda, U. & Sarkar, A. K. A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning. Front. Hum. Neurosci. 14(18), 1347082. https:\/\/doi.org\/10.3389\/fnhum.2024.1347082 (2024).","journal-title":"Front. Hum. Neurosci."},{"issue":"3","key":"96052_CR24","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0299127","volume":"19","author":"S Khan","year":"2024","unstructured":"Khan, S. et al. A machine learning based depression screening framework using temporal domain features of the electroencephalography signals. PLoS ONE 19(3), e0299127. https:\/\/doi.org\/10.1371\/journal.pone.0299127 (2024).","journal-title":"PLoS ONE"},{"key":"96052_CR25","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s10916-024-02048-0","volume":"48","author":"G Aksoy","year":"2024","unstructured":"Aksoy, G. et al. Quantum machine-based decision support system for the detection of schizophrenia from EEG records. J. Med. Syst. 48, 29. https:\/\/doi.org\/10.1007\/s10916-024-02048-0 (2024).","journal-title":"J. Med. Syst."},{"key":"96052_CR26","doi-asserted-by":"publisher","unstructured":"Qiao, J. \"A systematic review of machine learning approaches for mental disorder prediction on social media. In 2020 international conference on computing and data science (CDS), Stanford, CA, USA, pp. 433\u2013438 (2020). https:\/\/doi.org\/10.1109\/CDS49703.2020.00091.","DOI":"10.1109\/CDS49703.2020.00091"},{"key":"96052_CR27","doi-asserted-by":"publisher","first-page":"23203","DOI":"10.1007\/s10489-023-04702-5","volume":"53","author":"S Garc\u00eda-Ponsoda","year":"2023","unstructured":"Garc\u00eda-Ponsoda, S. et al. Feature engineering of EEG applied to mental disorders: A systematic mapping study. Appl. Intell 53, 23203\u201323243. https:\/\/doi.org\/10.1007\/s10489-023-04702-5 (2023).","journal-title":"Appl. Intell"},{"key":"96052_CR28","doi-asserted-by":"publisher","unstructured":"Bhat, S. & Reeja, S. R. A comprehensive study on mental illness through speech and EEG using artificial intelligence. EAI Endorsed Trans. Pervasive Health Technol., 10 (2024). https:\/\/doi.org\/10.4108\/eetpht.10.5328.","DOI":"10.4108\/eetpht.10.5328"},{"key":"96052_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-024-18259-z","author":"K Erat","year":"2024","unstructured":"Erat, K. et al. Emotion recognition with EEG-based brain-computer interfaces: A systematic literature review. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-024-18259-z (2024).","journal-title":"Multimed Tools Appl."},{"key":"96052_CR30","unstructured":"Thwaites, A., Nimmo-Smith, I., Weiser, E., Soltan, A. & Marslen-Wilson, W. Kymata Atlas Dataset 3.01: Raw, publicly available EMEG data (2017)."},{"key":"96052_CR31","doi-asserted-by":"publisher","first-page":"19","DOI":"10.3389\/fnbot.2017.00019","volume":"11","author":"Z Yin","year":"2017","unstructured":"Yin, Z., Wang, Y., Liu, Li., Zhang, W. & Zhang, J. Cross-subject EEG feature selection for emotion recognition using transfer recursive feature elimination. Front. Neurorobot. 11, 19. https:\/\/doi.org\/10.3389\/fnbot.2017.00019 (2017).","journal-title":"Front. Neurorobot."},{"issue":"3","key":"96052_CR32","doi-asserted-by":"publisher","first-page":"805","DOI":"10.3390\/s25030805","volume":"25","author":"H-L Wei","year":"2025","unstructured":"Wei, H.-L., Guo, Y., He, F. & Zhao, Y. EEG signal processing techniques and applications\u20142nd edition. Sensors 25(3), 805. https:\/\/doi.org\/10.3390\/s25030805 (2025).","journal-title":"Sensors"},{"key":"96052_CR33","doi-asserted-by":"publisher","unstructured":"Suveetha Dhanaselvam, P. & Nadia Chellam, C. A review on preprocessing of EEG signal. In 2023 International conference on bio signals, images, and instrumentation (ICBSII), Chennai, India, pp. 1\u20137 (2023). https:\/\/doi.org\/10.1109\/ICBSII58188.2023.10181071.","DOI":"10.1109\/ICBSII58188.2023.10181071"},{"key":"96052_CR34","doi-asserted-by":"publisher","first-page":"107080","DOI":"10.1016\/j.bspc.2024.107080","volume":"100","author":"K Rawat","year":"2025","unstructured":"Rawat, K. & Sharma, T. PsyneuroNet architecture for multi-class prediction of neurological disorders. Biomed. Signal Process. Control 100, 107080. https:\/\/doi.org\/10.1016\/j.bspc.2024.107080 (2025).","journal-title":"Biomed. Signal Process. Control"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-96052-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-96052-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-96052-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T16:21:34Z","timestamp":1743783694000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-96052-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,3]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["96052"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-96052-0","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,3]]},"assertion":[{"value":"18 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 April 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":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"11379"}}