{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:21:26Z","timestamp":1740108086849,"version":"3.37.3"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T00:00:00Z","timestamp":1713312000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T00:00:00Z","timestamp":1713312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["no. 61904038","no. U1913216"],"award-info":[{"award-number":["no. 61904038","no. U1913216"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["no. 2021YFC0122700"],"award-info":[{"award-number":["no. 2021YFC0122700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003399","name":"Shanghai Municipal Science and Technology Commission","doi-asserted-by":"crossref","award":["no. 2021SHZDZX0103"],"award-info":[{"award-number":["no. 2021SHZDZX0103"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100017130","name":"Ji Hua Laboratory","doi-asserted-by":"publisher","award":["no. X190021TB190","no.X190021TB193"],"award-info":[{"award-number":["no. X190021TB190","no.X190021TB193"]}],"id":[{"id":"10.13039\/100017130","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s00521-024-09765-0","type":"journal-article","created":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T13:02:03Z","timestamp":1713358923000},"page":"11603-11621","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Preference detection of the humanoid robot face based on EEG and eye movement"],"prefix":"10.1007","volume":"36","author":[{"given":"Pengchao","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Mu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gege","family":"Zhan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aiping","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zuoting","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueze","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junkongshuai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lan","family":"Niu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianxiong","family":"Bin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lihua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4372-9531","authenticated-orcid":false,"given":"Xiaoyang","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,17]]},"reference":[{"issue":"46","key":"9765_CR1","doi-asserted-by":"publisher","first-page":"eabb6652","DOI":"10.1126\/scirobotics.abb6652","volume":"5","author":"F Bossi","year":"2020","unstructured":"Bossi F, Willemse C, Cavazza J, Marchesi S, Murino V, Wykowska A (2020) The human brain reveals resting state activity patterns that are predictive of biases in attitudes toward robots. Sci Robot 5(46):eabb6652","journal-title":"Sci Robot"},{"issue":"6206","key":"9765_CR2","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1126\/science.346.6206.188","volume":"346","author":"D Normile","year":"2014","unstructured":"Normile D (2014) In our own image. Science 346(6206):188\u2013189. https:\/\/doi.org\/10.1126\/science.346.6206.188","journal-title":"Science"},{"issue":"7","key":"9765_CR3","doi-asserted-by":"publisher","first-page":"1679","DOI":"10.1007\/s12369-020-00738-6","volume":"13","author":"M Laakasuo","year":"2021","unstructured":"Laakasuo M, Palom\u00e4ki J, K\u00f6bis N (2021) Moral uncanny valley: a robot\u2019s appearance moderates how its decisions are judged. Int J Soc Robot 13(7):1679\u20131688","journal-title":"Int J Soc Robot"},{"issue":"58","key":"9765_CR4","doi-asserted-by":"publisher","first-page":"eabc5044","DOI":"10.1126\/scirobotics.abc5044","volume":"6","author":"M Belkaid","year":"2021","unstructured":"Belkaid M, Kompatsiari K, De Tommaso D, Zablith I, Wykowska A (2021) Mutual gaze with a robot affects human neural activity and delays decision-making processes. Sci Robot 6(58):eabc5044","journal-title":"Sci Robot"},{"issue":"18","key":"9765_CR5","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.1097\/wnr.0b013e32832d5989","volume":"20","author":"S Luu","year":"2009","unstructured":"Luu S, Chau T (2009) Neural representation of degree of preference in the medial prefrontal cortex. NeuroReport 20(18):1581\u20131585. https:\/\/doi.org\/10.1097\/wnr.0b013e32832d5989","journal-title":"NeuroReport"},{"issue":"9","key":"9765_CR6","doi-asserted-by":"publisher","first-page":"8983","DOI":"10.1007\/s13369-021-05695-4","volume":"46","author":"M Aldayel","year":"2021","unstructured":"Aldayel M, Ykhlef M, Al-Nafjan A (2021) Consumers\u2019 preference recognition based on brain\u2013computer interfaces: advances, trends, and applications. Arab J Sci Eng 46(9):8983\u20138997. https:\/\/doi.org\/10.1007\/s13369-021-05695-4","journal-title":"Arab J Sci Eng"},{"issue":"4","key":"9765_CR7","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.3390\/app10041525","volume":"10","author":"M Aldayel","year":"2020","unstructured":"Aldayel M, Ykhlef M, Al-Nafjan A (2020) Deep learning for EEG-based preference classification in neuromarketing. Appl Sci-Basel 10(4):1525. https:\/\/doi.org\/10.3390\/app10041525","journal-title":"Appl Sci-Basel"},{"issue":"1","key":"9765_CR8","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","volume":"3","author":"S Koelstra","year":"2011","unstructured":"Koelstra S et al (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":"4","key":"9765_CR9","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1111\/psyp.12375","volume":"52","author":"AK Bauer","year":"2015","unstructured":"Bauer AK, Kreutz G, Herrmann CS (2015) Individual musical tempo preference correlates with EEG beta rhythm. Psychophysiology 52(4):600\u2013604. https:\/\/doi.org\/10.1111\/psyp.12375","journal-title":"Psychophysiology"},{"key":"9765_CR10","doi-asserted-by":"crossref","unstructured":"Nakamura T, Ito S-i, Mitsukura Y, Setokawa H (2009) A method for evaluating the degree of human's preference based on EEG analysis. In: 2009 fifth international conference on intelligent information hiding and multimedia signal processing, 2009. IEEE, pp 732\u2013735","DOI":"10.1109\/IIH-MSP.2009.196"},{"issue":"9","key":"9765_CR11","doi-asserted-by":"publisher","first-page":"e0138153","DOI":"10.1371\/journal.pone.0138153","volume":"10","author":"JH Kang","year":"2015","unstructured":"Kang JH, Kim SJ, Cho YS, Kim SP (2015) Modulation of alpha oscillations in the human EEG with facial preference. PLoS ONE 10(9):e0138153. https:\/\/doi.org\/10.1371\/journal.pone.0138153","journal-title":"PLoS ONE"},{"issue":"1","key":"9765_CR12","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1177\/0887302x16673157","volume":"35","author":"B Touchette","year":"2017","unstructured":"Touchette B, Lee SE (2017) Measuring neural responses to apparel product attractiveness: an application of frontal asymmetry theory. Cloth Text Res J 35(1):3\u201315. https:\/\/doi.org\/10.1177\/0887302x16673157","journal-title":"Cloth Text Res J"},{"key":"9765_CR13","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.ijpsycho.2016.11.005","volume":"111","author":"EE Smith","year":"2017","unstructured":"Smith EE, Reznik SJ, Stewart JL, Allen JJ (2017) Assessing and conceptualizing frontal EEG asymmetry: an updated primer on recording, processing, analyzing, and interpreting frontal alpha asymmetry. Int J Psychophysiol 111:98\u2013114. https:\/\/doi.org\/10.1016\/j.ijpsycho.2016.11.005","journal-title":"Int J Psychophysiol"},{"issue":"5","key":"9765_CR14","doi-asserted-by":"publisher","first-page":"1403","DOI":"10.1002\/hbm.24455","volume":"40","author":"C Jacques","year":"2019","unstructured":"Jacques C, Jonas J, Maillard L, Colnat-Coulbois S, Koessler L, Rossion B (2019) The inferior occipital gyrus is a major cortical source of the face-evoked N170: evidence from simultaneous scalp and intracerebral human recordings. Hum Brain Mapp 40(5):1403\u20131418. https:\/\/doi.org\/10.1002\/hbm.24455","journal-title":"Hum Brain Mapp"},{"key":"9765_CR15","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.neuroscience.2020.12.036","volume":"458","author":"S Caharel","year":"2021","unstructured":"Caharel S, Rossion B (2021) The N170 is sensitive to long-term (personal) familiarity of a face identity. Neuroscience 458:244\u2013255. https:\/\/doi.org\/10.1016\/j.neuroscience.2020.12.036","journal-title":"Neuroscience"},{"issue":"1","key":"9765_CR16","doi-asserted-by":"publisher","first-page":"76","DOI":"10.17691\/stm2019.11.1.09","volume":"11","author":"DN Podvigina","year":"2019","unstructured":"Podvigina DN, Prokopenya VK (2019) Role of familiarity in recognizing faces and words: an EEG study. Sovrem Tehnol V Med 11(1):76\u201382","journal-title":"Sovrem Tehnol V Med"},{"key":"9765_CR17","first-page":"2015","volume-title":"F. Routledge encyclopedia of interpreting studies","author":"KG Seeber","year":"2015","unstructured":"Seeber KG (2015) Eye tracking. In: P C (ed) F. Routledge encyclopedia of interpreting studies. Routledge, London, p 2015"},{"key":"9765_CR18","doi-asserted-by":"crossref","unstructured":"Jin S, Qing C, Xu X, Wang Y (2019) Emotion recognition using eye gaze based on shallow CNN with identity mapping. In: International conference on brain inspired cognitive systems, 2019. Springer, pp 65\u201375","DOI":"10.1007\/978-3-030-39431-8_7"},{"issue":"8","key":"9765_CR19","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1167\/9.8.385","volume":"9","author":"T Holmes","year":"2009","unstructured":"Holmes T, Zanker J (2009) I like what I see: using eye-movement statistics to detect image preference. J Vis 9(8):385\u2013385","journal-title":"J Vis"},{"key":"9765_CR20","doi-asserted-by":"crossref","unstructured":"Schweikert C, Gobin L, Xie S, Shimojo S, Frank Hsu D (2018) Preference prediction based on eye movement using multi-layer combinatorial fusion. In: International conference on brain informatics, 2018. Springer, pp 282\u2013293","DOI":"10.1007\/978-3-030-05587-5_27"},{"key":"9765_CR21","unstructured":"Zheng W-L, Dong B-N, Lu B-L (2014) Multimodal emotion recognition using EEG and eye tracking data. In: 2014 36th annual international conference of the IEEE engineering in medicine and biology society, 2014. IEEE, pp 5040\u20135043"},{"key":"9765_CR22","doi-asserted-by":"crossref","unstructured":"Shi Z-F, Zhou C, Zheng W-L, Lu B-L (2017) Attention evaluation with eye tracking glasses for EEG-based emotion recognition. In: 2017 8th international IEEE\/EMBS conference on neural engineering (NER), 2017. IEEE, pp 86\u201389","DOI":"10.1109\/NER.2017.8008298"},{"key":"9765_CR23","doi-asserted-by":"publisher","first-page":"46","DOI":"10.3389\/fnbot.2019.00046","volume":"13","author":"Y Su","year":"2019","unstructured":"Su Y, Li W, Bi N, Lv Z (2019) Adolescents environmental emotion perception by integrating EEG and eye movements. Front Neurorobot 13:46. https:\/\/doi.org\/10.3389\/fnbot.2019.00046","journal-title":"Front Neurorobot"},{"key":"9765_CR24","doi-asserted-by":"crossref","unstructured":"Zhao L-M, Li R, Zheng W-L, Lu B-L (2019) Classification of five emotions from EEG and eye movement signals: complementary representation properties. In: 2019 9th international IEEE\/EMBS conference on neural engineering (NER), 2019. IEEE, pp 611\u2013614","DOI":"10.1109\/NER.2019.8717055"},{"key":"9765_CR25","unstructured":"Lu Y, Zheng W-L, Li B, Lu B-L (2015) Combining eye movements and EEG to enhance emotion recognition. In: Twenty-fourth international joint conference on artificial intelligence, 2015."},{"issue":"3","key":"9765_CR26","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1109\/TCYB.2018.2797176","volume":"49","author":"WL Zheng","year":"2019","unstructured":"Zheng WL, Liu W, Lu Y, Lu BL, Cichocki A (2019) EmotionMeter: a multimodal framework for recognizing human emotions. IEEE Trans Cybern 49(3):1110\u20131122. https:\/\/doi.org\/10.1109\/TCYB.2018.2797176","journal-title":"IEEE Trans Cybern"},{"key":"9765_CR27","doi-asserted-by":"crossref","unstructured":"Huang Y, Ma W, Yang Y (2020) Eye movement experiment research on users\u2019 aesthetic preferences of car seats. In: 2020 13th international symposium on computational intelligence and design (ISCID), 2020. IEEE, pp 310\u2013313","DOI":"10.1109\/ISCID51228.2020.00075"},{"issue":"3","key":"9765_CR28","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1016\/j.ijresmar.2020.10.005","volume":"38","author":"A Hakim","year":"2021","unstructured":"Hakim A, Klorfeld S, Sela T, Friedman D, Shabat-Simon M, Levy DJ (2021) Machines learn neuromarketing: improving preference prediction from self-reports using multiple EEG measures and machine learning. Int J Res Mark 38(3):770\u2013791. https:\/\/doi.org\/10.1016\/j.ijresmar.2020.10.005","journal-title":"Int J Res Mark"},{"issue":"4","key":"9765_CR29","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1109\/TAFFC.2019.2901733","volume":"12","author":"S-E Moon","year":"2019","unstructured":"Moon S-E, Kim J-H, Kim S-W, Lee J-S (2019) Prediction of car design perception using EEG and gaze patterns. IEEE Trans Affect Comput 12(4):843\u2013856","journal-title":"IEEE Trans Affect Comput"},{"key":"9765_CR30","doi-asserted-by":"crossref","unstructured":"Liu Y et al (2019) Detection of humanoid robot design preferences using EEG and eye tracker. In: 2019 international conference on cyberworlds (CW), 2019. IEEE, pp 219\u2013224","DOI":"10.1109\/CW.2019.00044"},{"key":"9765_CR31","doi-asserted-by":"publisher","first-page":"103159","DOI":"10.1016\/j.ergon.2021.103159","volume":"88","author":"MM Li","year":"2022","unstructured":"Li MM, Guo F, Ren ZG, Duffy VG (2022) A visual and neural evaluation of the affective impression on humanoid robot appearances in free viewing. Int J Ind Ergonom 88:103159. https:\/\/doi.org\/10.1016\/j.ergon.2021.103159","journal-title":"Int J Ind Ergonom"},{"issue":"7","key":"9765_CR32","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.1080\/0144929X.2021.1876763","volume":"41","author":"F Guo","year":"2022","unstructured":"Guo F, Li M, Chen J, Duffy VG (2022) Evaluating users\u2019 preference for the appearance of humanoid robots via event-related potentials and spectral perturbations. Behav Inf Technol 41(7):1381\u20131397","journal-title":"Behav Inf Technol"},{"key":"9765_CR33","doi-asserted-by":"crossref","unstructured":"Zhao W, Zhao Z, Li C (2018) Discriminative-CCA promoted by EEG signals for physiological-based emotion recognition. In: 2018 first Asian conference on affective computing and intelligent interaction (ACII Asia), 2018. IEEE, pp 1\u20136","DOI":"10.1109\/ACIIAsia.2018.8470373"},{"key":"9765_CR34","doi-asserted-by":"publisher","first-page":"958","DOI":"10.1109\/TSC.2017.2735409","volume":"13","author":"X Zhang","year":"2020","unstructured":"Zhang X et al (2020) Fusing of electroencephalogram and eye movement with group sparse canonical correlation analysis for anxiety detection. IEEE Trans Affect Comput 13:958\u2013971","journal-title":"IEEE Trans Affect Comput"},{"key":"9765_CR35","doi-asserted-by":"crossref","unstructured":"Liu W, Zheng W-L, Lu B-L (2016) Emotion recognition using multimodal deep learning. In: International conference on neural information processing, 2016. Springer, pp 521\u2013529","DOI":"10.1007\/978-3-319-46672-9_58"},{"key":"9765_CR36","doi-asserted-by":"publisher","first-page":"164130","DOI":"10.1109\/Access.2020.3021994","volume":"8","author":"HL Zhang","year":"2020","unstructured":"Zhang HL (2020) Expression-EEG based collaborative multimodal emotion recognition using deep AutoEncoder. IEEE Access 8:164130\u2013164143. https:\/\/doi.org\/10.1109\/Access.2020.3021994","journal-title":"IEEE Access"},{"key":"9765_CR37","doi-asserted-by":"crossref","unstructured":"Guo J-J, Zhou R, Zhao L-M, Lu B-L (2019) Multimodal emotion recognition from eye image, eye movement and EEG using deep neural networks. In: 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC), 2019. IEEE, pp 3071\u20133074","DOI":"10.1109\/EMBC.2019.8856563"},{"key":"9765_CR38","doi-asserted-by":"crossref","unstructured":"Ouzar Y, Bousefsaf F, Djeldjli D, Maaoui C (2022) Video-based multimodal spontaneous emotion recognition using facial expressions and physiological signals. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2022, pp 2460\u20132469","DOI":"10.1109\/CVPRW56347.2022.00275"},{"key":"9765_CR39","doi-asserted-by":"publisher","first-page":"103395","DOI":"10.1016\/j.jvcir.2021.103395","volume":"82","author":"W Dias","year":"2022","unstructured":"Dias W et al (2022) Cross-dataset emotion recognition from facial expressions through convolutional neural networks. J Vis Commun Image Represent 82:103395","journal-title":"J Vis Commun Image Represent"},{"key":"9765_CR40","doi-asserted-by":"publisher","first-page":"267","DOI":"10.3389\/fnins.2013.00267","volume":"7","author":"A Gramfort","year":"2013","unstructured":"Gramfort A et al (2013) MEG and EEG data analysis with MNE-Python. Front Neurosci 7:267. https:\/\/doi.org\/10.3389\/fnins.2013.00267","journal-title":"Front Neurosci"},{"key":"9765_CR41","doi-asserted-by":"publisher","DOI":"10.1201\/9781315117256","volume-title":"A brief survey of quantitative EEG","author":"K Majumdar","year":"2017","unstructured":"Majumdar K (2017) A brief survey of quantitative EEG. CRC Press, Boca Raton"},{"key":"9765_CR42","doi-asserted-by":"crossref","unstructured":"Alsolamy M, Fattouh A (2016) Emotion estimation from EEG signals during listening to Quran using PSD features. In: 2016 7th international conference on computer science and information technology (CSIT), 2016. IEEE, pp 1\u20135","DOI":"10.1109\/CSIT.2016.7549457"},{"key":"9765_CR43","unstructured":"Kirke A, Miranda ER (2011) 'Combining EEG frontal asymmetry studies with affective algorithmic composition and expressive performance models. In: Citeseer, 2011"},{"key":"9765_CR44","doi-asserted-by":"crossref","unstructured":"Ramirez R, Vamvakousis Z (2012) Detecting emotion from EEG signals using the emotive epoc device. In: International conference on brain informatics, 2012. Springer, pp 175\u2013184","DOI":"10.1007\/978-3-642-35139-6_17"},{"key":"9765_CR45","doi-asserted-by":"publisher","first-page":"354","DOI":"10.3389\/fnins.2015.00354","volume":"9","author":"R Ramirez","year":"2015","unstructured":"Ramirez R, Palencia-Lefler M, Giraldo S, Vamvakousis Z (2015) Musical neurofeedback for treating depression in elderly people. Front Neurosci 9:354. https:\/\/doi.org\/10.3389\/fnins.2015.00354","journal-title":"Front Neurosci"},{"issue":"2","key":"9765_CR46","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1109\/T-AFFC.2011.37","volume":"3","author":"M Soleymani","year":"2011","unstructured":"Soleymani M, Pantic M, Pun T (2011) Multimodal emotion recognition in response to videos. IEEE Trans Affect Comput 3(2):211\u2013223","journal-title":"IEEE Trans Affect Comput"},{"issue":"9","key":"9765_CR47","doi-asserted-by":"publisher","first-page":"1777","DOI":"10.1037\/dev0000362","volume":"53","author":"C Cohrdes","year":"2017","unstructured":"Cohrdes C, Wrzus C, Frisch S, Riediger M (2017) Tune yourself in: valence and arousal preferences in music-listening choices from adolescence to old age. Dev Psychol 53(9):1777\u20131794","journal-title":"Dev Psychol"},{"issue":"1","key":"9765_CR48","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1177\/2394964315569625","volume":"1","author":"D Baldo","year":"2015","unstructured":"Baldo D, Parikh H, Piu Y, M\u00fcller K-M (2015) Brain waves predict success of new fashion products: a practical application for the footwear retailing industry. J Creat Value 1(1):61\u201371","journal-title":"J Creat Value"},{"key":"9765_CR49","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F et al (2011) \u201cScikit-learn: machine learning in python,\u201d (in English). J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"issue":"16","key":"9765_CR50","doi-asserted-by":"publisher","first-page":"12378","DOI":"10.1016\/j.eswa.2012.04.084","volume":"39","author":"RN Khushaba","year":"2012","unstructured":"Khushaba RN, Greenacre L, Kodagoda S, Louviere J, Burke S, Dissanayake G (2012) Choice modeling and the brain: a study on the electroencephalogram (EEG) of preferences. Expert Syst Appl 39(16):12378\u201312388. https:\/\/doi.org\/10.1016\/j.eswa.2012.04.084","journal-title":"Expert Syst Appl"},{"key":"9765_CR51","doi-asserted-by":"crossref","unstructured":"Khushaba RN, Kodagoda S, Dissanayake G, Greenacre L, Burke S, Louviere J (2012) A neuroscientific approach to choice modeling: electroencephalogram (EEG) and user preferences. In: The 2012 international joint conference on neural networks (IJCNN), 2012: IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN.2012.6252561"},{"key":"9765_CR52","doi-asserted-by":"crossref","unstructured":"Ali A et al (2022) EEG signals based choice classification for neuromarketing applications. In: A fusion of artificial intelligence and internet of things for emerging cyber systems, pp 371\u2013394, 2022","DOI":"10.1007\/978-3-030-76653-5_20"},{"key":"9765_CR53","doi-asserted-by":"publisher","first-page":"861270","DOI":"10.3389\/fnhum.2022.861270","volume":"16","author":"FR Mashrur","year":"2022","unstructured":"Mashrur FR et al (2022) BCI-based consumers\u2019 choice prediction from EEG signals: an intelligent neuromarketing framework. Front Hum Neurosci 16:861270","journal-title":"Front Hum Neurosci"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09765-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09765-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09765-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T11:19:26Z","timestamp":1719314366000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09765-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,17]]},"references-count":53,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["9765"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09765-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2024,4,17]]},"assertion":[{"value":"22 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 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":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}},{"value":"The experiment was approved by the Medical Ethics Committee of Jing\u2019an District Central Hospital of Shanghai (Ethics reference number: 2020\u20132029).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}