{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T17:26:47Z","timestamp":1776101207507,"version":"3.50.1"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T00:00:00Z","timestamp":1708646400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T00:00:00Z","timestamp":1708646400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The National Natural Science Foundation of China under Grant","award":["61373116"],"award-info":[{"award-number":["61373116"]}]},{"name":"The National Natural Science Foundation of China under Grant","award":["62002287"],"award-info":[{"award-number":["62002287"]}]},{"name":"The Shaanxi Provincial Key Research and Development Project under Grant","award":["2022SF-037"],"award-info":[{"award-number":["2022SF-037"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s00521-024-09479-3","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T17:03:02Z","timestamp":1708707782000},"page":"7903-7922","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Emotion recognition based on phase-locking value brain functional network and topological data analysis"],"prefix":"10.1007","volume":"36","author":[{"given":"Zhong-min","family":"Wang","sequence":"first","affiliation":[]},{"given":"Sha","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Liang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,23]]},"reference":[{"key":"9479_CR1","unstructured":"Kang Q, Gao Q, Song Y, Tian Z, Yang Y, Mao Z, Dong E (2021) Emotion recognition from deaf eeg signals using stacking ensemble learning frame-work based on a novel brain network. IEEE Sens J, 08:1\u20131"},{"issue":"2","key":"9479_CR2","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1080\/02699930802204677","volume":"23","author":"IB Mauss","year":"2009","unstructured":"Mauss IB, Robinson MD (2009) Measures of emotion: a review. Cognit Emotion 23(2):209\u2013237","journal-title":"Cognit Emotion"},{"key":"9479_CR3","doi-asserted-by":"crossref","unstructured":"Mao Y, Song L, Zeng X (2021) Emotion classification from short-term eeg signals in deep learning. In: 2021 3rd International symposium on smart and healthy cities (ISHC), pp 169\u2013173. IEEE","DOI":"10.1109\/ISHC54333.2021.00038"},{"key":"9479_CR4","doi-asserted-by":"crossref","unstructured":"Park C, Ryu J, Sohn J, Cho H (2007) An emotion expression system for the emotional robot. In: 2007 IEEE international symposium on consumer electronics, pp 1\u20136","DOI":"10.1109\/ISCE.2007.4382195"},{"issue":"3","key":"9479_CR5","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1109\/TAFFC.2017.2712143","volume":"10","author":"W-L 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 Trans Affect Comput 10(3):417\u2013429","journal-title":"IEEE Trans Affect Comput"},{"key":"9479_CR6","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.compeleceng.2018.09.022","volume":"72","author":"T Chen","year":"2018","unstructured":"Chen T, Ju S, Yuan X, Elhoseny M, Ren F, Fan M, Chen Z (2018) Emotion recognition using empirical mode decomposition and approximation entropy. Comput Electrical Eng 72:383\u2013392","journal-title":"Comput Electrical Eng"},{"key":"9479_CR7","doi-asserted-by":"crossref","unstructured":"Zheng W-L, Guo H-T, Lu B-L (2015) Revealing critical channels and frequency bands for emotion recognition from eeg with deep belief network. In: 2015 7th International IEEE\/EMBS conference on neural engineering (NER), pp 154\u2013157. IEEE","DOI":"10.1109\/NER.2015.7146583"},{"key":"9479_CR8","volume":"100","author":"Y Yin","year":"2021","unstructured":"Yin Y, Zheng X, Hu B, Zhang Y, Cui X (2021) Eeg emotion recognition using fusion model of graph convolutional neural networks and lstm. Appl Soft Comput 100:106954","journal-title":"Appl Soft Comput"},{"issue":"2","key":"9479_CR9","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.neuroimage.2011.04.070","volume":"58","author":"M Ahmadlou","year":"2011","unstructured":"Ahmadlou M, Adeli H (2011) Functional community analysis of brain: a new approach for eeg-based investigation of the brain pathology. Neuroimage 58(2):401\u2013408","journal-title":"Neuroimage"},{"key":"9479_CR10","unstructured":"Wang ZM, Zhou R, He Y, Guo XM (2020) Functional integration and separation of brain network based on phase locking value during emotion processing. IEEE Trans Cognit Develop Syst, 99:1"},{"issue":"21","key":"9479_CR11","doi-asserted-by":"crossref","first-page":"8198","DOI":"10.3390\/s22218198","volume":"22","author":"V-R Xefteris","year":"2022","unstructured":"Xefteris V-R, Tsanousa A, Georgakopoulou N, Diplaris S, Vrochidis S, Kompatsiaris I (2022) Graph theoretical analysis of eeg functional connectivity patterns and fusion with physiological signals for emotion recognition. Sensors 22(21):8198","journal-title":"Sensors"},{"key":"9479_CR12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.future.2021.01.010","volume":"119","author":"Y Liu","year":"2021","unstructured":"Liu Y, Fu G (2021) Emotion recognition by deeply learned multi-channel textual and eeg features. Future Generation Comput Syst 119:1\u20136","journal-title":"Future Generation Comput Syst"},{"issue":"1","key":"9479_CR13","doi-asserted-by":"crossref","first-page":"18659","DOI":"10.1038\/s41598-022-22079-2","volume":"12","author":"T Adamovich","year":"2022","unstructured":"Adamovich T, Zakharov I, Tabueva A, Malykh S (2022) The thresholding problem and variability in the eeg graph network parameters. Sci Rep 12(1):18659","journal-title":"Sci Rep"},{"key":"9479_CR14","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1109\/TNSRE.2023.3239913","volume":"31","author":"R Katmah","year":"2023","unstructured":"Katmah R, Al-Shargie F, Tariq U, Babiloni F, Al-Mughairbi F, Al-Nashash H (2023) Mental stress management using fnirs directed connectivity and audio stimulation. IEEE Trans Neural Syst Rehabil Eng 31:1086\u20131096","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"6","key":"9479_CR15","doi-asserted-by":"crossref","first-page":"3552","DOI":"10.1364\/BOE.455097","volume":"13","author":"F Al-Shargie","year":"2022","unstructured":"Al-Shargie F, Katmah R, Tariq U, Babiloni F, Al-Mughairbi F, Al-Nashash H (2022) Stress management using fnirs and binaural beats stimulation. Biomed Opt Exp 13(6):3552\u20133575","journal-title":"Biomed Opt Exp"},{"key":"9479_CR16","volume":"15","author":"X Xiaoqi","year":"2021","unstructured":"Xiaoqi X, Drougard N, Roy RN (2021) Topological data analysis as a new tool for eeg processing. Front Neurosci 15:761703","journal-title":"Front Neurosci"},{"key":"9479_CR17","doi-asserted-by":"crossref","unstructured":"Wagner H, D\u0142otko P, Mrozek M (2012) Computational topology in text mining. In: Computational topology in image context: 4th international workshop, CTIC 2012, Bertinoro, Italy, May 28\u201330, 2012. Proceedings, pp 68\u201378. Springer","DOI":"10.1007\/978-3-642-30238-1_8"},{"key":"9479_CR18","doi-asserted-by":"crossref","unstructured":"Seversky LM, Davis S, Berger M (2016) On time-series topological data analysis: new data and opportunities. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 59\u201367","DOI":"10.1109\/CVPRW.2016.131"},{"issue":"3","key":"9479_CR19","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1162\/netn_a_00073","volume":"3","author":"AE Sizemore","year":"2019","unstructured":"Sizemore AE, Phillips-Cremins JE, Ghrist R, Bassett DS (2019) The importance of the whole: topological data analysis for the network neuroscientist. Network Neurosci 3(3):656\u2013673","journal-title":"Network Neurosci"},{"key":"9479_CR20","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.neuroimage.2014.07.040","volume":"101","author":"A Khalid","year":"2014","unstructured":"Khalid A, Kim BS, Chung MK, Ye JC, Jeon D (2014) Tracing the evolution of multi-scale functional networks in a mouse model of depression using persistent brain network homology. NeuroImage 101:351\u2013363","journal-title":"NeuroImage"},{"key":"9479_CR21","doi-asserted-by":"crossref","first-page":"1024205","DOI":"10.3389\/fncom.2022.1024205","volume":"16","author":"G Guo","year":"2022","unstructured":"Guo G, Zhao Y, Liu C, Fu Y, Xi X, Jin L, Shi D, Wang L, Duan Y, Huang J et al (2022) Method for persistent topological features extraction of schizophrenia patients\u2019 electroencephalography signal based on persistent homology. Front Comput Neurosci 16:1024205","journal-title":"Front Comput Neurosci"},{"issue":"3","key":"9479_CR22","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1214\/17-AOAS1119","volume":"12","author":"Y Wang","year":"2018","unstructured":"Wang Y, Ombao H, Chung MK (2018) Topological data analysis of single-trial electroencephalographic signals. Ann Appl Stat 12(3):1506","journal-title":"Ann Appl Stat"},{"issue":"2","key":"9479_CR23","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1109\/TCDS.2022.3174209","volume":"15","author":"Y Yan","year":"2023","unstructured":"Yan Y, Wu X, Li C, He Y, Zhang Z, Li H, Li A, Wang L (2023) Topological eeg nonlinear dynamics analysis for emotion recognition. IEEE Trans Cognit Develop Syst 15(2):625\u2013638","journal-title":"IEEE Trans Cognit Develop Syst"},{"key":"9479_CR24","doi-asserted-by":"crossref","first-page":"108","DOI":"10.3389\/frai.2021.667963","volume":"4","author":"F Chazal","year":"2021","unstructured":"Chazal F, Michel B (2021) An introduction to topological data analysis: fundamental and practical aspects for data scientists. Front Artif Intell 4:108","journal-title":"Front Artif Intell"},{"key":"9479_CR25","first-page":"1","volume":"71","author":"C Chen","year":"2022","unstructured":"Chen C, Li Z, Wan F, Leicai X, Bezerianos A, Wang H (2022) Fusing frequency-domain features and brain connectivity features for cross-subject emotion recognition. IEEE Trans Instrument Measure 71:1\u201315","journal-title":"IEEE Trans Instrument Measure"},{"issue":"3","key":"9479_CR26","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/0013-4694(70)90143-4","volume":"29","author":"B Hjorth","year":"1970","unstructured":"Hjorth B (1970) Eeg analysis based on time domain properties. Electroencephalogr Clin Neurophysiol 29(3):306\u2013310","journal-title":"Electroencephalogr Clin Neurophysiol"},{"key":"9479_CR27","doi-asserted-by":"crossref","unstructured":"Liu Y, Sourina O (2013) Real-time fractal-based valence level recognition from eeg. In: Transactions on computational science XVIII: special issue on cyberworlds, pp 101\u2013120. Springer","DOI":"10.1007\/978-3-642-38803-3_6"},{"issue":"2","key":"9479_CR28","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1109\/TITB.2009.2034649","volume":"14","author":"PC Petrantonakis","year":"2009","unstructured":"Petrantonakis PC, Hadjileontiadis LJ (2009) Emotion recognition from eeg using higher order crossings. IEEE Trans Inf Technol Biomed 14(2):186\u2013197","journal-title":"IEEE Trans Inf Technol Biomed"},{"key":"9479_CR29","doi-asserted-by":"crossref","unstructured":"Nie D, Wang X-W, Shi L-C, Lu B-L (2011) Eeg-based emotion recognition during watching movies. In: 2011 5th International IEEE\/EMBS conference on neural engineering, pp 667\u2013670. IEEE","DOI":"10.1109\/NER.2011.5910636"},{"key":"9479_CR30","doi-asserted-by":"crossref","unstructured":"Shi L-C, Jiao Y-Y, Lu B-L (2013) Differential entropy feature for eeg-based vigilance estimation. In: 2013 35th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 6627\u20136630. IEEE","DOI":"10.1109\/EMBC.2013.6611075"},{"key":"9479_CR31","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neucom.2013.06.046","volume":"129","author":"X-W Wang","year":"2014","unstructured":"Wang X-W, Nie D, Lu B-L (2014) Emotional state classification from eeg data using machine learning approach. Neurocomputing 129:94\u2013106","journal-title":"Neurocomputing"},{"key":"9479_CR32","first-page":"1","volume":"70","author":"Z Pei","year":"2020","unstructured":"Pei Z, Wang H, Bezerianos A, Li J (2020) Eeg-based multiclass workload identification using feature fusion and selection. IEEE Trans Instrument Measure 70:1\u20138","journal-title":"IEEE Trans Instrument Measure"},{"key":"9479_CR33","doi-asserted-by":"crossref","unstructured":"Wang H, Liu X, Li J, XuT, Bezerianos A, Sun Y, Wan F (2020) Driving fatigue recognition with functional connectivity based on phase synchronization. IEEE Trans Cognit Develop Syst 13(3):668\u2013678","DOI":"10.1109\/TCDS.2020.2985539"},{"key":"9479_CR34","doi-asserted-by":"crossref","first-page":"93711","DOI":"10.1109\/ACCESS.2019.2927768","volume":"7","author":"Z Wang","year":"2019","unstructured":"Wang Z, Tong Y, Heng X (2019) Phase-locking value based graph convolutional neural networks for emotion recognition. IEEE Access 7:93711\u201393722","journal-title":"IEEE Access"},{"issue":"14","key":"9479_CR35","doi-asserted-by":"crossref","first-page":"17327","DOI":"10.1007\/s10489-022-04366-7","volume":"53","author":"Z-M Wang","year":"2023","unstructured":"Wang Z-M, Chen Z-Y, Zhang J (2023) Eeg emotion recognition based on plv-rich-club dynamic brain function network. Appl Intell 53(14):17327\u201317345","journal-title":"Appl Intell"},{"issue":"10","key":"9479_CR36","doi-asserted-by":"crossref","first-page":"12064","DOI":"10.1007\/s10489-021-03070-2","volume":"52","author":"Z-M Wang","year":"2022","unstructured":"Wang Z-M, Zhang J-W, He Y, Zhang J (2022) Eeg emotion recognition using multichannel weighted multiscale permutation entropy. Appl Intell 52(10):12064\u201312076","journal-title":"Appl Intell"},{"issue":"10","key":"9479_CR37","doi-asserted-by":"crossref","first-page":"2869","DOI":"10.1109\/TBME.2019.2897651","volume":"66","author":"P Li","year":"2019","unstructured":"Li P, Liu H, Si Y, Li C, Li F, Zhu X, Huang X, Zeng Y, Yao D, Zhang Y et al (2019) Eeg based emotion recognition by combining functional connectivity network and local activations. IEEE Trans Biomed Eng 66(10):2869\u20132881","journal-title":"IEEE Trans Biomed Eng"},{"key":"9479_CR38","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2019.101563","volume":"53","author":"B Safarbali","year":"2019","unstructured":"Safarbali B, Golpayegani SMRH (2019) Nonlinear dynamic approaches to identify atrial fibrillation progression based on topological methods. Biomed Signal Process Control 53:101563","journal-title":"Biomed Signal Process Control"},{"issue":"14","key":"9479_CR39","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.ifacol.2018.07.222","volume":"51","author":"FA Khasawneh","year":"2018","unstructured":"Khasawneh FA, Munch E, Perea JA (2018) Chatter classification in turning using machine learning and topological data analysis. IFAC-PapersOnLine 51(14):195\u2013200","journal-title":"IFAC-PapersOnLine"},{"key":"9479_CR40","doi-asserted-by":"crossref","unstructured":"Chung MK, Hanson JL, Lee H, Adluru N, Alexander AL, Davidson RJ, Pollak SD (2013) Persistent homological sparse network approach to detecting white matter abnormality in maltreated children: Mri and dti multimodal study. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2013: 16th international conference, Nagoya, Japan, September 22\u201326, 2013, Proceedings, Part I 16, pp 300\u2013307. Springer","DOI":"10.1007\/978-3-642-40811-3_38"},{"key":"9479_CR41","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s10827-017-0672-6","volume":"44","author":"AE Sizemore","year":"2018","unstructured":"Sizemore AE, Giusti C, Kahn A, Vettel JM, Betzel RF, Bassett DS (2018) Cliques and cavities in the human connectome. J Computat Neurosci 44:115\u2013145","journal-title":"J Computat Neurosci"},{"issue":"3","key":"9479_CR42","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.ijpsycho.2014.10.010","volume":"94","author":"X Gao","year":"2014","unstructured":"Gao X, Cao H, Ming D, Qi H, Wang X, Wang X, Chen R, Zhou P (2014) Analysis of eeg activity in response to binaural beats with different frequencies. Int J Psychophysiol 94(3):399\u2013406","journal-title":"Int J Psychophysiol"},{"key":"9479_CR43","doi-asserted-by":"crossref","unstructured":"Zheng R, Wang Z, He Y, Zhang J (2022) Eeg-based brain functional connectivity representation using amplitude locking value for fatigue-driving recognition. Cognit Neurodyn, pp 1\u201312","DOI":"10.1007\/s11571-021-09714-w"},{"key":"9479_CR44","unstructured":"SA Nasab , Panahi S, Ghassemi F, Jafari S, Rajagopal K Ghosh D, Perc M (2021) Functional neuronal networks reveal emotional processing differences in children with adhd. Cognit Neurodyn, pp 1\u201310"},{"issue":"9","key":"9479_CR45","doi-asserted-by":"crossref","first-page":"3844","DOI":"10.1016\/j.cnsns.2010.12.031","volume":"16","author":"JRC Piqueira","year":"2011","unstructured":"Piqueira JRC (2011) Network of phase-locking oscillators and a possible model for neural synchronization. Commun Nonlinear Sci Numer Simul 16(9):3844\u20133854","journal-title":"Commun Nonlinear Sci Numer Simul"},{"issue":"1","key":"9479_CR46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s41109-019-0179-3","volume":"4","author":"ME Aktas","year":"2019","unstructured":"Aktas ME, Akbas E, El Fatmaoui A (2019) Persistence homology of networks: methods and applications. Appl Netw Sci 4(1):1\u201328","journal-title":"Appl Netw Sci"},{"key":"9479_CR47","doi-asserted-by":"crossref","unstructured":"Attali D, Lieutier A, Salinas D (2011) Vietoris-rips complexes also provide topologically correct reconstructions of sampled shapes. In: Proceedings of the twenty-seventh annual symposium on computational geometry, pp 491\u2013500","DOI":"10.1145\/1998196.1998276"},{"issue":"3","key":"9479_CR48","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.cag.2010.03.007","volume":"34","author":"A Zomorodian","year":"2010","unstructured":"Zomorodian A (2010) Fast construction of the vietoris-rips complex. Comput Graphics 34(3):263\u2013271","journal-title":"Comput Graphics"},{"issue":"1","key":"9479_CR49","first-page":"1834","volume":"22","author":"G Tauzin","year":"2021","unstructured":"Tauzin G, Lupo U, Tunstall L, P\u00e9rez JB, Caorsi M, Medina-Mardones AM, Dassatti A, Hess K (2021) giotto-tda: a topological data analysis toolkit for machine learning and data exploration. J Mach Learn Res 22(1):1834\u20131839","journal-title":"J Mach Learn Res"},{"key":"9479_CR50","doi-asserted-by":"crossref","unstructured":"Cohen-Steiner D, Edelsbrunner H, Harer J (2005) Stability of persistence diagrams. In: Proceedings of the twenty-first annual symposium on Computational geometry, pp 263\u2013271","DOI":"10.1145\/1064092.1064133"},{"key":"9479_CR51","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.jsc.2016.03.009","volume":"78","author":"P Bubenik","year":"2017","unstructured":"Bubenik P, D\u0142otko P (2017) A persistence landscapes toolbox for topological statistics. J Symbol Comput 78:91\u2013114","journal-title":"J Symbol Comput"},{"key":"9479_CR52","doi-asserted-by":"crossref","unstructured":"Rucco M, Castiglione F, Merelli E, Pettini M (2016) Characterisation of the idiotypic immune network through persistent entropy. In: Proceedings of ECCS 2014: European conference on complex systems, pp 117\u2013128. Springer","DOI":"10.1007\/978-3-319-29228-1_11"},{"key":"9479_CR53","doi-asserted-by":"crossref","unstructured":"Garin A, Tauzin G (2019) A topological\" reading\" lesson: classification of mnist using tda. In: 2019 18th IEEE international conference on machine learning and applications (ICMLA), pp 1551\u20131556. IEEE","DOI":"10.1109\/ICMLA.2019.00256"},{"issue":"3","key":"9479_CR54","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/TAFFC.2018.2817622","volume":"11","author":"T Song","year":"2018","unstructured":"Song T, Zheng W, Song P, Cui Z (2018) Eeg emotion recognition using dynamical graph convolutional neural networks. IEEE Trans Affect Comput 11(3):532\u2013541","journal-title":"IEEE Trans Affect Comput"},{"issue":"1","key":"9479_CR55","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","volume":"3","author":"S Koelstra","year":"2011","unstructured":"Koelstra S, Muhl C, Soleymani M, Lee J-S, Yazdani A, Ebrahimi T, Pun T, Nijholt A, Patras I (2011) Deap: a database for emotion analysis; using physiological signals. IEEE Trans Affect Comput 3(1):18\u201331","journal-title":"IEEE Trans Affect Comput"},{"issue":"1","key":"9479_CR56","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/JBHI.2017.2688239","volume":"22","author":"S Katsigiannis","year":"2017","unstructured":"Katsigiannis S, Ramzan N (2017) Dreamer: a database for emotion recognition through eeg and ecg signals from wireless low-cost off-the-shelf devices. IEEE J Biomed Health Informat 22(1):98\u2013107","journal-title":"IEEE J Biomed Health Informat"},{"issue":"3","key":"9479_CR57","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","volume":"7","author":"W-L Zheng","year":"2015","unstructured":"Zheng W-L, Bao-Liang L (2015) Investigating critical frequency bands and channels for eeg-based emotion recognition with deep neural networks. IEEE Trans Autonom Mental Develop 7(3):162\u2013175","journal-title":"IEEE Trans Autonom Mental Develop"},{"key":"9479_CR58","doi-asserted-by":"crossref","unstructured":"Duan R-N, Zhu J-Y, Lu B-L (2013) Differential entropy feature for eeg-based emotion classification. In: 2013 6th International IEEE\/EMBS conference on neural engineering (NER), pp 81\u201384. IEEE","DOI":"10.1109\/NER.2013.6695876"},{"key":"9479_CR59","volume":"79","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Yan G, Chang W, Huang W, Yuan Y (2023) Eeg-based multi-frequency band functional connectivity analysis and the application of spatio-temporal features in emotion recognition. Biomed Signal Process Control 79:104157","journal-title":"Biomed Signal Process Control"},{"key":"9479_CR60","volume":"761","author":"W Wang","year":"2021","unstructured":"Wang W (2021) Brain network features based on theta-gamma cross-frequency coupling connections in eeg for emotion recognition. Neurosci Lett 761:136106","journal-title":"Neurosci Lett"},{"key":"9479_CR61","doi-asserted-by":"crossref","first-page":"33002","DOI":"10.1109\/ACCESS.2020.2974009","volume":"8","author":"H Chao","year":"2020","unstructured":"Chao H, Liu Y (2020) Emotion recognition from multi-channel eeg signals by exploiting the deep belief-conditional random field framework. IEEE Access 8:33002\u201333012","journal-title":"IEEE Access"},{"key":"9479_CR62","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.eswa.2015.10.049","volume":"47","author":"J Atkinson","year":"2016","unstructured":"Atkinson J, Campos D (2016) Improving bci-based emotion recognition by combining eeg feature selection and kernel classifiers. Exp Syst Appl 47:35\u201341","journal-title":"Exp Syst Appl"},{"key":"9479_CR63","doi-asserted-by":"crossref","unstructured":"Xu H, Wang X, Li W, Wang H, Bi Q (2019) Research on eeg channel selection method for emotion recognition. In: 2019 IEEE international conference on robotics and biomimetics (ROBIO), pp 2528\u20132535. IEEE","DOI":"10.1109\/ROBIO49542.2019.8961740"},{"issue":"1","key":"9479_CR64","doi-asserted-by":"crossref","first-page":"3769","DOI":"10.1038\/s41598-023-30458-6","volume":"13","author":"G Cui","year":"2023","unstructured":"Cui G, Li X, Touyama H (2023) Emotion recognition based on group phase locking value using convolutional neural network. Sci Rep 13(1):3769","journal-title":"Sci Rep"},{"key":"9479_CR65","doi-asserted-by":"crossref","unstructured":"Wang Y, Ombao H, Chung MK (2019) Statistical persistent homology of brain signals. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1125\u20131129. IEEE","DOI":"10.1109\/ICASSP.2019.8682978"},{"key":"9479_CR66","doi-asserted-by":"crossref","unstructured":"Majumder S, Apicella F, Muratori F, Das K (2020) Detecting autism spectrum disorder using topological data analysis. In: ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1210\u20131214. IEEE","DOI":"10.1109\/ICASSP40776.2020.9054747"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09479-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09479-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09479-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T17:39:31Z","timestamp":1731433171000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09479-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,23]]},"references-count":66,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["9479"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09479-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,23]]},"assertion":[{"value":"24 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All authors approve.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"All authors agree.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All authors read and approved the final manuscript.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}