{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T10:27:03Z","timestamp":1776940023478,"version":"3.51.4"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1-2","license":[{"start":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T00:00:00Z","timestamp":1734912000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T00:00:00Z","timestamp":1734912000000},"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":["Wireless Pers Commun"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s11277-024-11656-5","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T16:16:35Z","timestamp":1734970595000},"page":"165-192","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["EEG Signal Based Human Emotion Recognition Brain-computer Interface using Deep Learning and High-Performance Computing"],"prefix":"10.1007","volume":"140","author":[{"given":"Vinay Kumar","family":"Singh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiv","family":"Prakash","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pratibha","family":"Dixit","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mukesh","family":"Prasad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,23]]},"reference":[{"key":"11656_CR1","unstructured":"Joshi, S., and Joshi, F.(2022). Human emotion classification based on EEG signals using recurrent neural network and KNN, May 2022, [Online]. Available: http:\/\/arxiv.org\/abs\/2205.08419"},{"key":"11656_CR2","doi-asserted-by":"publisher","first-page":"12527","DOI":"10.1007\/s00521-022-07292-4","volume":"34","author":"EH Houssein","year":"2022","unstructured":"Houssein, E. H., Hammad, A., & Ali, A. A. (2022). Human emotion recognition from EEG-based brain-computer interface using machine learning: a comprehensive review. Neural Computing and Applications, 34, 12527\u201312557. https:\/\/doi.org\/10.1007\/s00521-022-07292-4","journal-title":"Neural Computing and Applications"},{"key":"11656_CR3","unstructured":"Zhong, P., Wang, D., and Miao, C. (2019). EEG-Based Emotion Recognition Using Regularized Graph Neural Networks, Jul. 2019, [Online]. Available: http:\/\/arxiv.org\/abs\/1907.07835"},{"issue":"3","key":"11656_CR4","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1109\/TAFFC.2017.2712143","volume":"10","author":"WL Zheng","year":"2019","unstructured":"Zheng, W. L., Zhu, J. Y., & Lu, B. L. (2019). Identifying stable patterns over time for emotion recognition from eeg. IEEE Transactions on Affective Computing, 10(3), 417\u2013429. https:\/\/doi.org\/10.1109\/TAFFC.2017.2712143","journal-title":"IEEE Transactions on Affective Computing"},{"issue":"August","key":"11656_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fncom.2019.00053","volume":"13","author":"F Yang","year":"2019","unstructured":"Yang, F., Zhao, X., Jiang, W., Gao, P., & Liu, G. (2019). Multi-method fusion of cross-subject emotion recognition based on high-dimensional EEG features. Frontiers in Computational Neuroscience, 13(August), 1\u201311. https:\/\/doi.org\/10.3389\/fncom.2019.00053","journal-title":"Frontiers in Computational Neuroscience"},{"key":"11656_CR6","doi-asserted-by":"publisher","first-page":"103338","DOI":"10.1016\/j.bspc.2021.103338","volume":"72","author":"X Zhao","year":"2022","unstructured":"Zhao, X., Liu, D., Ma, L., Liu, Q., Chen, K., Xie, S., & Ai, Q. (2022). Biomedical Signal Processing and Control Deep CNN model based on serial-parallel structure optimization for four-class motor imagery EEG classification. Biomedical Signal Processing and Control, 72, 103338. https:\/\/doi.org\/10.1016\/j.bspc.2021.103338","journal-title":"Biomedical Signal Processing and Control"},{"issue":"4","key":"11656_CR7","doi-asserted-by":"publisher","first-page":"67","DOI":"10.5815\/ijmecs.2022.04.06","volume":"14","author":"JM Vala","year":"2022","unstructured":"Vala, J. M., & Jaliya, U. K. (2022). Deep learning network and renyi-entropy based fusion model for emotion recognition using multimodal signals. International Journal of Modern Education and Computer Science, 14(4), 67\u201384. https:\/\/doi.org\/10.5815\/ijmecs.2022.04.06","journal-title":"International Journal of Modern Education and Computer Science"},{"key":"11656_CR8","doi-asserted-by":"publisher","DOI":"10.3389\/fnsys.2020.00043","author":"J Liu","year":"2020","unstructured":"Liu, J., Wu, G., Luo, Y., Qiu, S., Yang, S., Li, W., & Bi, Y. (2020). EEG-based emotion classification using a deep neural network and sparse autoencoder. Frontiers System Neuroscience. https:\/\/doi.org\/10.3389\/fnsys.2020.00043","journal-title":"Frontiers System Neuroscience"},{"key":"11656_CR9","doi-asserted-by":"publisher","unstructured":"Du, B., Liu, Y., and Tian, G. (2021). Improving motor imagery EEG classification by CNN with data augmentation. https:\/\/doi.org\/10.1109\/iccicc50026.2020.9450227.","DOI":"10.1109\/iccicc50026.2020.9450227"},{"key":"11656_CR10","doi-asserted-by":"publisher","first-page":"24520","DOI":"10.1109\/ACCESS.2022.3155647","volume":"10","author":"A Samavat","year":"2022","unstructured":"Samavat, A., Khalili, E., Ayati, B., & Ayati, M. (2022). Deep learning model with adaptive regularization for EEG-based emotion recognition using temporal and frequency features. IEEE Access, 10, 24520\u201324527. https:\/\/doi.org\/10.1109\/ACCESS.2022.3155647","journal-title":"IEEE Access"},{"key":"11656_CR11","doi-asserted-by":"publisher","DOI":"10.1186\/s40708-021-00141-5","author":"P Patel","year":"2021","unstructured":"Patel, P., Raghunandan, R., & Annavarapu, R. N. (2021). EEG-based human emotion recognition using entropy as a feature extraction measure. Brain Informatics. https:\/\/doi.org\/10.1186\/s40708-021-00141-5","journal-title":"Brain Informatics"},{"issue":"7","key":"11656_CR12","doi-asserted-by":"publisher","first-page":"2953","DOI":"10.1007\/s00371-022-02502-5","volume":"39","author":"P Santhiya","year":"2023","unstructured":"Santhiya, P., & Chitrakala, S. (2023). PTCERE: Personality-trait mapping using cognitive-based emotion recognition from electroencephalogram signals. The Visual Computer, 39(7), 2953\u20132967. https:\/\/doi.org\/10.1007\/s00371-022-02502-5","journal-title":"The Visual Computer"},{"key":"11656_CR13","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci11111525","author":"M Saeidi","year":"2021","unstructured":"Saeidi, M., Karwowski, W., Farahani, F. V., Fiok, K., Taiar, R., Hancock, P. A., & Al-Juaid, A. (2021). Neural decoding of EEG signals with machine learning: A systematic review. Brain Sciences. https:\/\/doi.org\/10.3390\/brainsci11111525","journal-title":"Brain Sciences"},{"key":"11656_CR14","doi-asserted-by":"publisher","unstructured":"Mbeledogu, N. Stock feature extraction using principal component analysis, https:\/\/doi.org\/10.7763\/IPCSIT.2012.V47.44.","DOI":"10.7763\/IPCSIT.2012.V47.44"},{"key":"11656_CR15","doi-asserted-by":"publisher","first-page":"2264","DOI":"10.1016\/j.egypro.2017.12.628","volume":"142","author":"Z Chen","year":"2017","unstructured":"Chen, Z., & Xiong, R. (2017). Driving cycle development for electric vehicle application using principal component analysis and k-means cluster: With the case of Shenyang, China. Energy Procedia, 142, 2264\u20132269. https:\/\/doi.org\/10.1016\/j.egypro.2017.12.628","journal-title":"Energy Procedia"},{"key":"11656_CR16","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.cogr.2022.06.001","volume":"2","author":"C Yu","year":"2022","unstructured":"Yu, C., & Wang, M. (2022). Survey of emotion recognition methods using EEG information. Cognitive Robotics, 2, 132\u2013146. https:\/\/doi.org\/10.1016\/j.cogr.2022.06.001","journal-title":"Cognitive Robotics"},{"issue":"1","key":"11656_CR17","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/s13246-015-0333-x","volume":"38","author":"HU Amin","year":"2015","unstructured":"Amin, H. U., Malik, A. S., Ahmad, R. F., Badruddin, N., Kamel, N., Hussain, M., & Chooi, W. T. (2015). 2015 Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques,\". Australas Phys Eng Sci Med, 38(1), 139\u2013149. https:\/\/doi.org\/10.1007\/s13246-015-0333-x","journal-title":"Australas Phys Eng Sci Med"},{"key":"11656_CR18","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-07517-5","author":"LA Moctezuma","year":"2022","unstructured":"Moctezuma, L. A., Abe, T., & Molinas, M. (2022). Two-dimensional CNN-based distinction of human emotions from EEG channels selected by multi-objective evolutionary algorithm. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-022-07517-5","journal-title":"Sci Rep"},{"key":"11656_CR19","doi-asserted-by":"publisher","unstructured":"Wu, W. Member., Chen, Z. Senior Member, Gao, X. Member., Li, Y. Member., Gao, S. Fellow., & Wu, W. (2015). Probabilistic common spatial patterns for multichannel EEG analysis HHS public access IEEE Trans Pattern Analysis Machine Intelligence, 373, 639\u2013653, , https:\/\/doi.org\/10.1109\/TPAMI.","DOI":"10.1109\/TPAMI"},{"issue":"1","key":"11656_CR20","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1080\/27706710.2022.2075241","volume":"1","author":"W Qian","year":"2022","unstructured":"Qian, W., Tan, J., Jiang, Y., & Tian, Y. (2022). Deep learning with convolutional neural networks for EEG-based music emotion decoding and visualization. Brain-Apparatus Communication: A Journal of Bacomics, 1(1), 38\u201349. https:\/\/doi.org\/10.1080\/27706710.2022.2075241","journal-title":"Brain-Apparatus Communication: A Journal of Bacomics"},{"key":"11656_CR21","unstructured":"Vakili, M., Ghamsari, M., & Rezaei, M.(2020). Performance analysis and comparison of machine and deep learning algorithms for IoT data classification."},{"key":"11656_CR22","unstructured":"Guo, G., Wang, H., Bell, D. A., Bi, Y., Bell, D., & Greer, K. (2004). KNN model-based approach in classification. [Online]. Available: https:\/\/www.researchgate.net\/publication\/2948052"},{"key":"11656_CR23","doi-asserted-by":"publisher","unstructured":"Awad, M., & Khanna, R. (2015). Support vector machines for classification. In: Efficient Learning Machines, Apress, pp. 39\u201366. https:\/\/doi.org\/10.1007\/978-1-4302-5990-9_3.","DOI":"10.1007\/978-1-4302-5990-9_3"},{"key":"11656_CR24","doi-asserted-by":"publisher","first-page":"2387","DOI":"10.3390\/electronics11152387","volume":"11","author":"MK Chowdary","year":"2022","unstructured":"Chowdary, M. K., Anitha, J., & Hemanth, D. J. (2022). Emotion recognition from EEG signals using recurrent neural networks. Electronics, 11, 2387. https:\/\/doi.org\/10.3390\/electronics11152387","journal-title":"Electronics"},{"key":"11656_CR25","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling Dec. 2014, [Online]. Available: http:\/\/arxiv.org\/abs\/1412.3555"},{"key":"11656_CR26","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.622759","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Chen, J., Tan, J. H., Chen, Y., Chen, Y., Li, D., Yang, L., Jian, S., Huang, X., & Che, W. (2020). An investigation of deep learning models for EEG-based emotion recognition. Front Neurosci. https:\/\/doi.org\/10.3389\/fnins.2020.622759","journal-title":"Front Neurosci"},{"key":"11656_CR27","doi-asserted-by":"crossref","unstructured":"Li, X., Song, D., Zhang, P., Yu, G., Hou, Y., and Hu, B. (2016). Emotion recognition from multi-channel EEG data through convolutional recurrent neural network. In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2016, pp. 352\u2013359.","DOI":"10.1109\/BIBM.2016.7822545"},{"issue":"3","key":"11656_CR28","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1109\/TAFFC.2017.2712143","volume":"10","author":"WL Zheng","year":"2017","unstructured":"Zheng, W. L., Zhu, J. Y., & Lu, B. L. (2017). Identifying stable patterns over time for emotion recognition from EEG. IEEE Transactions on Affective Computing, 10(3), 417\u2013429.","journal-title":"IEEE Transactions on Affective Computing"},{"issue":"4","key":"11656_CR29","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1109\/TAFFC.2017.2660485","volume":"9","author":"Y-J Liu","year":"2017","unstructured":"Liu, Y.-J., Minjing, Y., Zhao, G., Song, J., Ge, Y., & Shi, Y. (2017). Real-time movie-induced discrete emotion recognition from EEG signals. IEEE Transactions on Affective Computing, 9(4), 550\u2013562.","journal-title":"IEEE Transactions on Affective Computing"},{"key":"11656_CR30","doi-asserted-by":"publisher","first-page":"44317","DOI":"10.1109\/ACCESS.2019.2908285","volume":"7","author":"JX Chen","year":"2019","unstructured":"Chen, J. X., Zhang, P. W., Mao, Z. J., Huang, Y. F., Jiang, D. M., & Zhang, Y. N. (2019). Accurate EEG-based emotion recognition on combined features using deep convolutional neural networks. IEEE Access, 7, 44317\u201344328.","journal-title":"IEEE Access"},{"issue":"910","key":"11656_CR31","first-page":"926","volume":"40","author":"R Nawaz","year":"2020","unstructured":"Nawaz, R., Cheah, K. H., Nisar, H., & Yap, V. V. (2020). Comparison of different feature extraction methods for EEG-based emotion recognition. Biocybernetics Biomedical Engineering, 40(910), 926.","journal-title":"Biocybernetics Biomedical Engineering"},{"issue":"6","key":"11656_CR32","doi-asserted-by":"publisher","first-page":"1442","DOI":"10.1016\/j.jestch.2021.03.012","volume":"24","author":"F Topic","year":"2021","unstructured":"Topic, F., & Russo, M. (2021). Emotion recognition based on EEG feature maps through deep learning network. Engineering Science and Technology, an International Journal, 24(6), 1442.","journal-title":"Engineering Science and Technology, an International Journal"},{"key":"11656_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.108740","volume":"122","author":"D Li","year":"2022","unstructured":"Li, D., Xie, L., Chai, B., Wang, Z., & Yang, H. (2022). Spatial-frequency convolutional self-attention network for EEG emotion recognition. Applied Soft Computing, 122, 108740.","journal-title":"Applied Soft Computing"},{"issue":"3","key":"11656_CR34","doi-asserted-by":"publisher","first-page":"1622","DOI":"10.3390\/s23031622","volume":"23","author":"X Zhang","year":"2023","unstructured":"Zhang, X., Li, Y., Jinxiang, D., Zhao, R., Kemeng, X., Zhang, L., & She, Y. (2023). Feature pyramid networks and long short-term memory for EEG feature map-based emotion recognition. Sensors, 23(3), 1622.","journal-title":"Sensors"},{"key":"11656_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104620","volume":"83","author":"D Kuang","year":"2023","unstructured":"Kuang, D., & Michoski, C. (2023). SEER-Net: Simple EEG-based recognition network. Biomedical Signal Processing and Control, 83, 104620.","journal-title":"Biomedical Signal Processing and Control"},{"key":"11656_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104211","volume":"79","author":"MY Zhong","year":"2023","unstructured":"Zhong, M. Y., Yang, Q. Y., Liu, Y., Zhen, B. Y., & Xie, B. B. (2023). EEG emotion recognition based on TQWT-features and hybrid convolutional recurrent neural network. Biomed Signal Process Control, 79, 104211.","journal-title":"Biomed Signal Process Control"},{"key":"11656_CR37","doi-asserted-by":"publisher","first-page":"1364","DOI":"10.1007\/s12559-023-10171-2","volume":"16","author":"T Dhara","year":"2024","unstructured":"Dhara, T., Singh, P. K., & Mahmud, M. (2024). A fuzzy ensemble-based deep learning model for EEG-based emotion recognition. Cognitive Computation, 16, 1364\u20131378.","journal-title":"Cognitive Computation"},{"key":"11656_CR38","doi-asserted-by":"publisher","first-page":"107927","DOI":"10.1016\/j.cmpb.2023.107927","volume":"243","author":"Xu FeiFan","year":"2024","unstructured":"FeiFan, Xu., Pan, D., Haohao Zheng, Y., Ouyang, Z. J., & Zeng, H. (2024). EESCN: A novel spiking neural network method for EEG-based emotion recognition. Comput Methods Programs Biomed., 243, 107927.","journal-title":"Comput Methods Programs Biomed."},{"key":"11656_CR39","doi-asserted-by":"crossref","unstructured":"Rathore RS, Sangwan S, Prakash S, Adhikari K, Kharel R, Cao Y. Hybrid WGWO: whale grey wolf optimization-based novel energy-efficient clustering for EH-WSNs. EURASIP Journal on Wireless Communications and Networking. 2020 Dec;2020:1-28.","DOI":"10.1186\/s13638-020-01721-5"},{"key":"11656_CR40","doi-asserted-by":"crossref","unstructured":"Jha SK, Prakash S, Rathore RS, Mahmud M, Kaiwartya O, Lloret J. Quality-of-service-centric design and analysis of unmanned aerial vehicles. Sensors. 2022 Jul 22;22(15):5477","DOI":"10.3390\/s22155477"},{"key":"11656_CR41","doi-asserted-by":"crossref","unstructured":"Kumar, B. A., Jyothi, B., Singh, A. R., Bajaj, M., Rathore, R. S., & Tuka, M. B. (2024). Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience. Scientific Reports, 14(1), 7637.","DOI":"10.1038\/s41598-024-58024-8"},{"key":"11656_CR42","doi-asserted-by":"crossref","unstructured":"Ashraf, M. W. A., Singh, A. R., Pandian, A., Rathore, R. S., Bajaj, M., & Zaitsev, I. (2024). A hybrid approach using support vector machine rule-based system: detecting cyber threats in internet of things. Scientific Reports, 14(1), 27058.","DOI":"10.1038\/s41598-024-78976-1"},{"key":"11656_CR43","doi-asserted-by":"crossref","unstructured":"Akram, J., Anaissi, A., Rathore, R. S., Jhaveri, R. H., & Akram, A. (2024). Galtrust: Generative adverserial learning-based framework for trust management in spatial crowdsourcing drone services. IEEE Transactions on Consumer Electronics.","DOI":"10.1109\/TCE.2024.3384978"}],"updated-by":[{"DOI":"10.1007\/s11277-025-11736-0","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T00:00:00Z","timestamp":1736899200000}}],"container-title":["Wireless Personal Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-024-11656-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11277-024-11656-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-024-11656-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T12:49:52Z","timestamp":1741783792000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11277-024-11656-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,23]]},"references-count":43,"journal-issue":{"issue":"1-2","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["11656"],"URL":"https:\/\/doi.org\/10.1007\/s11277-024-11656-5","relation":{},"ISSN":["0929-6212","1572-834X"],"issn-type":[{"value":"0929-6212","type":"print"},{"value":"1572-834X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,23]]},"assertion":[{"value":"11 November 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2025","order":3,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":4,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The original version of this article was revised: In this article the heading for Section 2 was incorrectly given as \u2018These contents are shifted before organization of the paper\u2019 but should have been \u2018Human Emotion\u2019. The original article has been corrected.","order":5,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2025","order":6,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":7,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":8,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s11277-025-11736-0","URL":"https:\/\/doi.org\/10.1007\/s11277-025-11736-0","order":9,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}