{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T22:06:00Z","timestamp":1791497160761,"version":"4.3.4"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,15]],"date-time":"2025-03-15T00:00:00Z","timestamp":1741996800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,15]],"date-time":"2025-03-15T00:00:00Z","timestamp":1741996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Manipal University Jaipur"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Brain-Computer Interface (BCI) is a versatile technique to offer better communication system for people affected by the locked-in syndrome (LIS).In the current decade, there has been a growing demand for improved care and services for individuals with neurodegenerative diseases. To address this barrier, the current work is designed with four states of BCI for paralyzed persons using Welch Power Spectral Density (W-PSD). The features extracted from the signals were trained with a hybrid Feed Forward Neural Network Cheetah Optimization Algorithm (FFNNCOA) in both offline and online modes. Totally, eighteen subjects were involved in this study. The study proved that the offline analysis phase outperformed than the online phase in the real-time. The experiment was achieved the accuracies of 95.56% and 93.88% for men and female respectively. Furthermore, the study confirms that the subject\u2019s performance in the offline can manage the task more easily than in online mode.<\/jats:p>","DOI":"10.1038\/s41598-025-93047-9","type":"journal-article","created":{"date-parts":[[2025,3,15]],"date-time":"2025-03-15T07:02:13Z","timestamp":1742022133000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Simulating online and offline tasks using hybrid cheetah optimization algorithm for patients affected by neurodegenerative diseases"],"prefix":"10.1038","volume":"15","author":[{"given":"Ramkumar","family":"Sivasakthivel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manikandan","family":"Rajagopal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G.","family":"Anitha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K.","family":"Loganathan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Abbas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amel","family":"Ksibi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koppula Srinivas","family":"Rao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,15]]},"reference":[{"key":"93047_CR1","doi-asserted-by":"publisher","unstructured":"Ragagnin, S. Vidal,Jamali, and Atkin, motor neuron susceptibility in ALS\/FTD. Front. NeuroSci. 13 (532). https:\/\/doi.org\/10.3389\/fnins.2019.00532 (2019).","DOI":"10.3389\/fnins.2019.00532"},{"key":"93047_CR2","unstructured":"Padhy Artificial Neural Network in Artificial Intelligence and Intelligent Systems (Oxford University Press, 2005)."},{"key":"93047_CR3","unstructured":"Sivanandam, P. Introduction To Articial Neural Networks (Vikas Publishing House, 2003)."},{"key":"93047_CR4","unstructured":"Sivanandam, D. Principles of Soft Computing (Wiley Publishing House, 2007)."},{"key":"93047_CR5","doi-asserted-by":"publisher","unstructured":"Thilagaraj, R., Arunkumar, D., Hariharasitaraman, K., Rajasekaran, P. & Govindan. Classification of electroencephalogram signal for developing brain-computer interface using bioinspired machine learning approach. Comput. Intell. Neurosci.   4487254, 17 (2022). https:\/\/doi.org\/10.1155\/2022\/4487254.","DOI":"10.1155\/2022\/4487254"},{"key":"93047_CR6","doi-asserted-by":"publisher","unstructured":"Jayabrabu Ramakrishnan, D., Mavaluru, R. S.,  Alqahtani, S.A.S., Mubarakali, A. &  Retnadhas, M. Brain\u2013computer interface for amyotrophic lateral sclerosis patients using deep learning network. Neural Comput. Appl. 34, 13439\u201313453. https:\/\/doi.org\/10.1007\/s00521-020-05026-y (2022).","DOI":"10.1007\/s00521-020-05026-y"},{"issue":"4","key":"93047_CR7","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1109\/TPAMI.2007.1053","volume":"29","author":"DP Mandic","year":"2007","unstructured":"Mandic, D. P. & R.Palaniappan, & Biometrics from brain electrical activity: A machine learning approach. IEEE Trans. Pattern Anal. Mach. Intell. 29 (4), 738\u2013742. https:\/\/doi.org\/10.1109\/TPAMI.2007.1053 (2007).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"93047_CR8","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1109\/TIFS.2014.2308640","volume":"9","author":"DL Rocca","year":"2014","unstructured":"Rocca, D. L. & P.Campisi, & Brain waves for automatic biometric-based user recognition. IEEE Trans. Inf. Forensics Secur. 9 (5), 782\u2013800. https:\/\/doi.org\/10.1109\/TIFS.2014.2308640 (2014).","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"93047_CR9","doi-asserted-by":"publisher","unstructured":"S.Marcel, & J.D.R.Mill\u00e1n, Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE Trans. Pattern Anal. Mach. Intell., 29(4),  743\u2013752 (2007). https:\/\/doi.org\/10.1109\/TPAMI.2007.1054","DOI":"10.1109\/TPAMI.2007.1054"},{"key":"93047_CR10","doi-asserted-by":"publisher","unstructured":"Nicolas-Alonso, L.F. & Gomez-Gil, J. Brain computer interfaces, A review Sensors 12(2),  1211\u20131279. https:\/\/doi.org\/10.3390\/s120201211 (2012).","DOI":"10.3390\/s120201211"},{"issue":"7","key":"93047_CR11","first-page":"169","volume":"5","author":"C Shivkumar, Michahial","year":"2015","unstructured":"Shivkumar, Michahial, C. Classification and speech synthesizer for disables. Indian J. Appl. Res. 5 (7), 169\u2013172 (2015).","journal-title":"Indian J. Appl. Res."},{"key":"93047_CR12","unstructured":"Ramkumar, K. & Emayavaramban Speech synthesizer for persons with speech impairment. Indian Patent 201841044575 (2018)."},{"key":"93047_CR13","doi-asserted-by":"crossref","unstructured":"Williams, L. & Ekanayake, P.A. Facilitating communication and computer use with EEG devices for non-vocal quadriplegics. In  International Conference on Information and Automation for Sustainability (2014).","DOI":"10.1109\/ICIAFS.2014.7069604"},{"key":"93047_CR14","doi-asserted-by":"crossref","unstructured":"Lee, L. & Hwang, I.  Society Development of a hybrid mental speller combining EEG-based brain-computer interface and webcam-based eye-tracking. In  International Conference of the IEEE Engineering in Medicine and Biology (EMBC). 2240\u2009\u2013\u20092013.","DOI":"10.1109\/EMBC.2013.6609982"},{"key":"93047_CR15","doi-asserted-by":"crossref","unstructured":"Spuler, A.  Brain-computer interface (BCI) system to use arbitrary windows applications by directly controlling mouse and keyboard. In  International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2015).","DOI":"10.1109\/EMBC.2015.7318554"},{"key":"93047_CR16","unstructured":"Janik, P. & Gardecki, C. & Baranowski, B. Game controller based on biomedical signals. In  IEEE International Conference on Methods and Models in Automation and Robotics (MMAR) (2015)."},{"key":"93047_CR17","doi-asserted-by":"crossref","unstructured":"Vasavi, R. & Radhika, P. A mind operated computer mouse using discrete wavelet transforms for elderly people with multiple disabilities. In  International Conference on Computational Modeling and Security, Procedia Computer Science. Vol. 85.166\u2013175 (2016).","DOI":"10.1016\/j.procs.2016.05.205"},{"key":"93047_CR18","doi-asserted-by":"crossref","unstructured":"Wolpaw, M. &  Neat,  F. An EEG-based brain-computer interface for cursor control. Electroencephalogr. Clin. Neurophysiol. 78(3), 252\u2013259 (1991).","DOI":"10.1016\/0013-4694(91)90040-B"},{"key":"93047_CR19","doi-asserted-by":"crossref","unstructured":"McFarland, K., Sarnacki,  W. Emulation of computer mouse control with a noninvasive brain-computer interface. J. Neural Eng. 5 (2), 101\u2013110 (2008).","DOI":"10.1088\/1741-2560\/5\/2\/001"},{"key":"93047_CR20","doi-asserted-by":"crossref","unstructured":"Fok, S., Wronkiewicz, H.,  Zhang, S. &  Bundy, L. An EEG-based brain computer interface for rehabilitation and restoration of hand control following stroke using ipsilateral cortical physiology. In  International Conference of the IEEE EMBS Boston. 6277\u20136280 (2011).","DOI":"10.1109\/IEMBS.2011.6091549"},{"key":"93047_CR21","doi-asserted-by":"crossref","unstructured":"English, H. & Kesten, L. Jin EyePhone: A mobile EOG-based human-computer interface for assistive healthcare. In  International IEEE\/EMBS Conference on Neural Engineering (NER) (2013).","DOI":"10.1109\/NER.2013.6695882"},{"key":"93047_CR22","doi-asserted-by":"crossref","unstructured":"Zubi. Detecting facial expressions from EEG signals and head movement for controlling mouse curser. In  International Conference on Innovative Computing Technology (INTECH 2013). 551\u2013554 (2013).","DOI":"10.1109\/INTECH.2013.6653651"},{"key":"93047_CR23","doi-asserted-by":"crossref","unstructured":"Huang, Z., Yu, H. &  Li. An EEG-\/EOG-based hybrid brain-computer interface: Application on controlling an integrated wheelchair robotic arm system. Front. NeuroSci., 13, 1243 (2019).","DOI":"10.3389\/fnins.2019.01243"},{"key":"93047_CR24","doi-asserted-by":"publisher","unstructured":"Katyal, E.A. & Singla , R. EEG-based hybrid QWERTY mental speller with high information transfer rate. Med. Biol. Eng. Comput. 59, 633\u2013661. https:\/\/doi.org\/10.1007\/s11517-020-02310-w (2021).","DOI":"10.1007\/s11517-020-02310-w"},{"key":"93047_CR25","doi-asserted-by":"publisher","unstructured":"Pellico Sanchez, O.I.,   Nino-Suarez, P.A., Hern\u00e1ndez-Beleno, R.D., Avies-Sanchez, O.F. &  P\u00e9rez-Bahena, M.H. Classification of electrooculography signals using convolutional neural networks for interaction with a manipulator robot. Smart Innov. Syst. Technol. 318, 13\u201323. https:\/\/doi.org\/10.1007\/978-981-19-6347-6_2 (2023).","DOI":"10.1007\/978-981-19-6347-6_2"},{"key":"93047_CR26","doi-asserted-by":"publisher","unstructured":"Ogino, M., Hamada, N. & Mitsukura, Y. Simultaneous multiple-stimulus auditory brain\u2013computer interface with semi-supervised learning and prior probability distribution tuning. IEEE Sens. J. 19 (6). https:\/\/doi.org\/10.1088\/1741-2552\/ac9edd (2022).","DOI":"10.1088\/1741-2552\/ac9edd"},{"key":"93047_CR27","doi-asserted-by":"publisher","unstructured":"Ban, Q. & Feng, P.  A hybrid brain-computer interface for smart car control. In Human Brain and Artificial Intelligence. HBAI 2022. Communications in Computer and Information Science (Ying, X. eds.). Vol. 1692. 135\u2013147 (Springer, 2023).  https:\/\/doi.org\/10.1007\/978-981-19-8222-4_12","DOI":"10.1007\/978-981-19-8222-4_12"},{"key":"93047_CR28","doi-asserted-by":"publisher","unstructured":"Pawar, D. &  Dhage, S. EEG-based covert speech decoding using random rotation extreme learning machine ensemble for intuitive BCI communication. Biomed. Signal Process. Control 80(Part 2), 104379 (2023). https:\/\/doi.org\/10.1016\/j.bspc.2022.104379","DOI":"10.1016\/j.bspc.2022.104379"},{"key":"93047_CR29","doi-asserted-by":"publisher","unstructured":"Zhu, Z.  Ding, Y. &  Xu, Z. An improved feature extraction method using low-rank representation for motor imagery classification. Biomed. Signal Process. Control. 80 (2). https:\/\/doi.org\/10.1016\/j.bspc.2022.104389 (pp.104389, 2023).","DOI":"10.1016\/j.bspc.2022.104389"},{"key":"93047_CR30","doi-asserted-by":"publisher","unstructured":"Li, C.  et al. EEG-based seizure prediction via model uncertainty learning. IEEE Trans. Neural Syst. Rehabil. Eng. https:\/\/doi.org\/10.1109\/TNSRE.2022.3217929 (2022).","DOI":"10.1109\/TNSRE.2022.3217929"},{"key":"93047_CR31","doi-asserted-by":"publisher","unstructured":"Das, K. &  Pachori, R.B. Electroencephalogram based motor imagery brain computer interface using multivariate iterative filtering and spatial filtering. IEEE Trans. Cogn. Dev. Syst. https:\/\/doi.org\/10.1109\/TCDS.2022.3214081 (2022).","DOI":"10.1109\/TCDS.2022.3214081"},{"key":"93047_CR32","doi-asserted-by":"publisher","unstructured":"Shi, T., Ren, S. & Cui, H.  An optimized artificial intelligence based technique for identifying motor imagery from EEGs for advanced brain computer interface technology. Neural Comput. Appl. https:\/\/doi.org\/10.1007\/s00521-022-08027-1 (2022).","DOI":"10.1007\/s00521-022-08027-1"},{"key":"93047_CR33","doi-asserted-by":"publisher","unstructured":"Smith, K.K.,  Gaikwad, N.  &  Bokde, N.D. An intelligent motor imagery detection system using electroencephalography with adaptive wavelets. Sensors 22, 8128 (2022). https:\/\/doi.org\/10.3390\/s22218128","DOI":"10.3390\/s22218128"},{"key":"93047_CR34","doi-asserted-by":"publisher","unstructured":"Pawan & Dhiman, R. EMotor imagery signal classification using wavelet packet decomposition and modified binary grey Wolf optimization. Meas. Sens. 24 (100553). https:\/\/doi.org\/10.1016\/j.measen.2022.100553 (2022).","DOI":"10.1016\/j.measen.2022.100553"},{"key":"93047_CR35","doi-asserted-by":"publisher","unstructured":"Kurczak, J., Bia\u0142as, K., Chalupnik, R. & Kedziora, M. Using brain-computer interface (BCI) and artificial intelligence for EEG signal analysis. In  Asian Conference on Intelligent Information and Database Systems, Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2022. Communications in Computer and Information Science. Vol. 318. 214\u2013226 (2023). https:\/\/doi.org\/10.1007\/978-981-19-8234-7_17","DOI":"10.1007\/978-981-19-8234-7_17"},{"key":"93047_CR36","doi-asserted-by":"publisher","unstructured":"Tong, J.  et al. Hybrid mental tasks based human computer interface via integration of pronunciation and motor imagery. J. Neural Eng. 19 (5), 056048. https:\/\/doi.org\/10.1088\/1741-2552\/ac9a01 (2022).","DOI":"10.1088\/1741-2552\/ac9a01"},{"key":"93047_CR37","doi-asserted-by":"publisher","unstructured":"Meng, M., Dong, Z., Gao, Y. & She, Q. Optimal channel and frequency band-based feature selection for motor imagery electroencephalogram classification. Int. J. Imaging Syst. Technol. https:\/\/doi.org\/10.1002\/ima.22823 (2022).","DOI":"10.1002\/ima.22823"},{"key":"93047_CR38","doi-asserted-by":"publisher","unstructured":"Pan, B. &  Zheng, W. Emotion recognition based on EEG using generative adversarial nets and convolutional neural network. Comput. Math. Methods Med. 2520394 (2021). https:\/\/doi.org\/10.1155\/2021\/2520394","DOI":"10.1155\/2021\/2520394"},{"key":"93047_CR39","doi-asserted-by":"publisher","unstructured":"Bao, G. et al. Data augmentation for EEG-based emotion recognition using generative adversarial networks. Front. Comput. Neurosci. 15(723843), 1\u201312.   https:\/\/doi.org\/10.3389\/fncom.2021.723843 (2021).","DOI":"10.3389\/fncom.2021.723843"},{"key":"93047_CR40","doi-asserted-by":"publisher","unstructured":"Kwon, O.Y.  et al. Subject-independent brain\u2013computer interfaces based on deep convolutional neural networks. IEEE Trans. Neural Networks Learn. Syst. 31 (10), 3839\u20133852. https:\/\/doi.org\/10.1109\/TNNLS.2019.2946869 (2020).","DOI":"10.1109\/TNNLS.2019.2946869"},{"key":"93047_CR41","doi-asserted-by":"publisher","unstructured":"Shi, T. et al.  Feature extraction of brain\u2013computer interface electroencephalogram based on motor imagery. IEEE Sens. J. 20 (20), 11787\u201311794. https:\/\/doi.org\/10.1109\/JSEN.2019.2939343 (2020).","DOI":"10.1109\/JSEN.2019.2939343"},{"key":"93047_CR42","doi-asserted-by":"publisher","unstructured":"Mishchenko, Y.  et al.  Developing a three- to six-state EEG-based brain\u2013computer interface for a virtual robotic manipulator control. IEEE Trans. Biomed. Eng. 66 (4), 977\u2013987. https:\/\/doi.org\/10.1109\/TBME.2018.2865941 (2019).","DOI":"10.1109\/TBME.2018.2865941"},{"key":"93047_CR43","doi-asserted-by":"publisher","unstructured":"AlQattan, S. Towards sign language recognition using EEG-based motor imagery brain computer interface. In  2017 5th International Winter Conference on Brain-Computer Interface (BCI) (2017).https:\/\/doi.org\/10.1109\/IWW-BCI.2017.7858143","DOI":"10.1109\/IWW-BCI.2017.7858143"},{"key":"93047_CR44","doi-asserted-by":"publisher","unstructured":"Abidi, O., Farooq & Beg, M.M.S. Sweet and sour taste classification using EEG based brain computer interface. In Annual IEEE India Conference (INDICON). https:\/\/doi.org\/10.1109\/INDICON.2015.7443230 (2015).","DOI":"10.1109\/INDICON.2015.7443230"},{"key":"93047_CR45","doi-asserted-by":"publisher","unstructured":"Murugappan, M., Nagarajan, R. & Yaacob, S. Comparison of different wavelet features from EEG signals for classifying human emotions. In IEEE Symposium on Industrial Electronics & Applications. Vol. 2 (2009). https:\/\/doi.org\/10.1109\/ISIEA.2009.5356339.","DOI":"10.1109\/ISIEA.2009.5356339"},{"key":"93047_CR46","doi-asserted-by":"publisher","unstructured":"Ameera, A., Saidatul, A. &  Ibrahim, Z. Analysis of EEG spectrum bands using power spectral density for pleasure and displeasure state. In  IOP Conference Series Material Science Engineering. Vol.  557. 1\u20135. https:\/\/doi.org\/10.1088\/1757-899X\/557\/1\/012030 (2019).","DOI":"10.1088\/1757-899X\/557\/1\/012030"},{"key":"93047_CR47","doi-asserted-by":"publisher","unstructured":"Alam, R.U. et al.  Differences in power spectral densities and phase quantities due to processing of EEG signals. Sensors 20(21), 6285  (2020). https:\/\/doi.org\/10.3390\/s20216285","DOI":"10.3390\/s20216285"},{"key":"93047_CR48","doi-asserted-by":"publisher","unstructured":"Xiong, Z., Wang, Gu, A. P. & Article, I. D. Algorithm Framework for Feature Extraction of EEG Signals on MPI, Computational and Mathematical Methods in Medicine. Vol. 9812019 (2020). https:\/\/doi.org\/10.1155\/2020\/9812019","DOI":"10.1155\/2020\/9812019"},{"key":"93047_CR49","unstructured":"Solom, P. S. D. Computations Using Welch\u2019s Method (Sandia National Laboratories , 1991)."},{"key":"93047_CR50","doi-asserted-by":"publisher","unstructured":"Jayabrabu, R. et al.  Electrooculogram-aided intelligent sensing and high-performance communication control system for massive ALS individuals. J. Supercomput. 77,  6961\u20136978. https:\/\/doi.org\/10.1007\/s11227-020-03517-2 (2021).","DOI":"10.1007\/s11227-020-03517-2"},{"key":"93047_CR51","doi-asserted-by":"publisher","unstructured":"Rastogi, A. K., Taterh, S. & Kumar, S. Bio-inspired algorithms for prey model optimization. In  International Conference on Innovative Practices in Technology and Management (ICIPTM) . https:\/\/doi.org\/10.1109\/ICIPTM54933.2022.9754200 (2022).","DOI":"10.1109\/ICIPTM54933.2022.9754200"},{"key":"93047_CR52","doi-asserted-by":"publisher","unstructured":"Saravanan, D., Paul, P.V.,  Janakiraman, S., Dumka, A. &  Jayakumar,  L. Bio-inspired algorithm based on the hunting behavior of cheetah.  Int. J.  Inf.  Technol. Proj. Manag. (IJITPM) IGI Glob. 11 (4), 13\u201330. https:\/\/doi.org\/10.4018\/IJITPM.2020100102 (2020).","DOI":"10.4018\/IJITPM.2020100102"},{"key":"93047_CR53","doi-asserted-by":"publisher","unstructured":"Darwish, A.  Bio-inspired computing: algorithms review, deep analysis, and the scope of applications. Future Comput. Inf. J. 3 (2), 231\u2013246. https:\/\/doi.org\/10.1016\/j.fcij.2018.06.001 (2018).","DOI":"10.1016\/j.fcij.2018.06.001"},{"key":"93047_CR54","doi-asserted-by":"publisher","unstructured":"Akbari, M. A., Zare, M., Azizipanah-abarghooee, R. & Mirjalili, S. The cheetah optimizer: A nature-inspired metaheuristic algorithm for large-scale optimization problems. Sci. Rep. 12 (10953). https:\/\/doi.org\/10.1038\/s41598-022-14338-z (2021).","DOI":"10.1038\/s41598-022-14338-z"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-93047-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-93047-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-93047-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,15]],"date-time":"2025-03-15T07:02:29Z","timestamp":1742022149000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-93047-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,15]]},"references-count":54,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["93047"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-93047-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,15]]},"assertion":[{"value":"23 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 March 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"}},{"value":"The study received approval from Kalasalingam Academy of Research and Education. All procedures were conducted in strict accordance with applicable guidelines and regulations. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"Written informed consent was obtained from all participants prior to their inclusion in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}}],"article-number":"8951"}}