{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T09:23:05Z","timestamp":1788513785371,"version":"build-2803163510"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T00:00:00Z","timestamp":1709683200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T00:00:00Z","timestamp":1709683200000},"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":["Cogn Neurodyn"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s11571-024-10090-4","type":"journal-article","created":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T08:02:25Z","timestamp":1709712145000},"page":"2277-2288","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Linear and nonlinear analysis of multimodal physiological data for affective arousal recognition"],"prefix":"10.1007","volume":"18","author":[{"given":"Ali","family":"Khaleghi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kian","family":"Shahi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maryam","family":"Saidi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nafiseh","family":"Babaee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Razieh","family":"Kaveh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amin","family":"Mohammadian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,6]]},"reference":[{"issue":"1","key":"10090_CR10","first-page":"1","volume":"13","author":"Y CHEN","year":"2017","unstructured":"CHEN Y, CUI Y (2017) Review of emotion recognition based on physiological signals. Syst Simul Technol 13(1):1\u20135","journal-title":"Syst Simul Technol"},{"key":"10090_CR28","doi-asserted-by":"publisher","first-page":"103291","DOI":"10.1109\/ACCESS.2019.2929311","volume":"7","author":"X Chen","year":"2019","unstructured":"Chen X, Hao X (2019) Feature reduction method for cognition and classification of IoT devices based on artificial intelligence. IEEE Access 7:103291\u2013103298","journal-title":"IEEE Access"},{"key":"10090_CR25","doi-asserted-by":"crossref","unstructured":"Dey S et al (2020) A comparative study of support vector machine and Naive Bayes classifier for sentiment analysis on Amazon product reviews. in. International Conference on Contemporary Computing and Applications (IC3A). 2020. IEEE","DOI":"10.1109\/IC3A48958.2020.233300"},{"issue":"3","key":"10090_CR8","first-page":"592","volume":"20","author":"A Dzedzickis","year":"2020","unstructured":"Dzedzickis A, Kaklauskas A, Bucinskas V (2020) Hum Emot Recognition: Rev Sens Methods Sens 20(3):592","journal-title":"Hum Emot Recognition: Rev Sens Methods Sens"},{"key":"10090_CR11","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.entcs.2019.04.009","volume":"343","author":"M Egger","year":"2019","unstructured":"Egger M, Ley M, Hanke S (2019) Emotion recognition from physiological signal analysis: a review. Electron Notes Theor Comput Sci 343:35\u201355","journal-title":"Electron Notes Theor Comput Sci"},{"issue":"1","key":"10090_CR1","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1177\/0146167218780695","volume":"45","author":"Y Ganzach","year":"2019","unstructured":"Ganzach Y, Yaor E (2019) The retrospective evaluation of positive and negative affect. Pers Soc Psychol Bull 45(1):93\u2013104","journal-title":"Pers Soc Psychol Bull"},{"issue":"1","key":"10090_CR14","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1109\/TAFFC.2019.2927337","volume":"13","author":"G Giannakakis","year":"2019","unstructured":"Giannakakis G et al (2019) Review on psychological stress detection using biosignals. IEEE Trans Affect Comput 13(1):440\u2013460","journal-title":"IEEE Trans Affect Comput"},{"key":"10090_CR5","doi-asserted-by":"crossref","unstructured":"Gillespie SM, Jones A, Garofalo C (2022) Psychopathy and dangerousness: An umbrella review and meta-analysis Clinical psychology review, : p. 102240","DOI":"10.1016\/j.cpr.2022.102240"},{"issue":"3","key":"10090_CR4","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1109\/JSEN.2016.2623677","volume":"17","author":"A Greco","year":"2016","unstructured":"Greco A et al (2016) Arousal and valence recognition of affective sounds based on electrodermal activity. IEEE Sens J 17(3):716\u2013725","journal-title":"IEEE Sens J"},{"issue":"1","key":"10090_CR22","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.advms.2018.08.003","volume":"64","author":"I Gruszczy\u0144ska","year":"2019","unstructured":"Gruszczy\u0144ska I et al (2019) Epilepsy identification based on EEG signal using RQA method. Adv Med Sci 64(1):58\u201364","journal-title":"Adv Med Sci"},{"key":"10090_CR17","doi-asserted-by":"crossref","unstructured":"Hong W-C (2020) Phase Space Reconstruction and recurrence plot theory, in Hybrid Intelligent technologies in Energy demand forecasting. Springer, pp 153\u2013179","DOI":"10.1007\/978-3-030-36529-5_6"},{"key":"10090_CR26","doi-asserted-by":"publisher","first-page":"105160","DOI":"10.1016\/j.bspc.2023.105160","volume":"86","author":"A Jain","year":"2023","unstructured":"Jain A, Kumar L (2023) Subject-independent trajectory prediction using pre-movement EEG during grasp and lift task. Biomed Signal Process Control 86:105160","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"10090_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42600-019-00036-9","volume":"36","author":"A Khaleghi","year":"2020","unstructured":"Khaleghi A et al (2020) Applicable features of electroencephalogram for ADHD diagnosis. Res Biomedical Eng 36(1):1\u201311","journal-title":"Res Biomedical Eng"},{"key":"10090_CR18","doi-asserted-by":"crossref","unstructured":"Khaleghi A et al (2021) A neuronal population model based on cellular automata to simulate the electrical waves of the brain. Waves Random Complex Media, : p. 1\u201320","DOI":"10.1080\/17455030.2021.1938746"},{"issue":"1","key":"10090_CR13","doi-asserted-by":"publisher","first-page":"26","DOI":"10.9758\/cpn.2022.20.1.26","volume":"20","author":"A Khaleghi","year":"2022","unstructured":"Khaleghi A et al (2022) Computational neuroscience approach to psychiatry: a review on theory-driven approaches. Clin Psychopharmacol Neurosci 20(1):26","journal-title":"Clin Psychopharmacol Neurosci"},{"key":"10090_CR21","doi-asserted-by":"crossref","unstructured":"Khaleghi A et al (2023) Possible neuropathological mechanisms underlying the increased complexity of Brain Electrical Activity in Schizophrenia: a computational study. Iran J Psychiatry, : p. 1\u20137","DOI":"10.18502\/ijps.v18i2.12363"},{"key":"10090_CR29","doi-asserted-by":"crossref","unstructured":"Kumov V, Samorodov A (2020) Recognition of genetic diseases based on combined feature extraction from 2D face images. in 26th Conference of Open Innovations Association (FRUCT). 2020. IEEE","DOI":"10.23919\/FRUCT48808.2020.9087456"},{"key":"10090_CR31","doi-asserted-by":"crossref","unstructured":"Li X et al (2016) Emotion recognition from multi-channel EEG data through convolutional recurrent neural network. in. IEEE international conference on bioinformatics and biomedicine (BIBM). 2016. IEEE","DOI":"10.1109\/BIBM.2016.7822545"},{"key":"10090_CR32","doi-asserted-by":"crossref","unstructured":"Liu J et al (2016) Emotion detection from EEG recordings. in. 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). 2016. IEEE","DOI":"10.1109\/FSKD.2016.7603437"},{"key":"10090_CR20","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.ijpsycho.2017.05.006","volume":"122","author":"A Mehrnam","year":"2017","unstructured":"Mehrnam A et al (2017) Reprint of a new approach to analyze data from EEG-based concealed face recognition system. Int J Psychophysiol 122:17\u201323","journal-title":"Int J Psychophysiol"},{"issue":"2","key":"10090_CR27","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1007\/s13534-016-0218-2","volume":"6","author":"MR Mohammadi","year":"2016","unstructured":"Mohammadi MR et al (2016) EEG classification of ADHD and normal children using non-linear features and neural network. Biomed Eng Lett 6(2):66\u201373","journal-title":"Biomed Eng Lett"},{"issue":"9","key":"10090_CR2","doi-asserted-by":"publisher","first-page":"1801","DOI":"10.1037\/dev0000789","volume":"55","author":"SD Pollak","year":"2019","unstructured":"Pollak SD, Camras LA, Cole PM (2019) Progress in understanding the emergence of human emotion. Dev Psychol 55(9):1801","journal-title":"Dev Psychol"},{"key":"10090_CR9","doi-asserted-by":"crossref","unstructured":"Ragot M et al (2017) Emotion recognition using physiological signals: laboratory vs. wearable sensors. in International Conference on Applied Human Factors and Ergonomics. Springer","DOI":"10.1007\/978-3-319-60639-2_2"},{"key":"10090_CR23","doi-asserted-by":"crossref","unstructured":"Seleznov I, Tukaiev S, Popov A (2019) Detrended fluctuation analysis of the effects of advertisement placement on electroencephalogram. in 2019 Signal Processing Symposium (SPSympo). IEEE","DOI":"10.1109\/SPS.2019.8881973"},{"issue":"4","key":"10090_CR6","doi-asserted-by":"publisher","first-page":"3429","DOI":"10.1109\/TVT.2022.3152980","volume":"71","author":"Y Shi","year":"2022","unstructured":"Shi Y et al (2022) Perception of driving simulations: can the level of Detail of virtual scenarios affect the driver\u2019s behavior and emotions? IEEE Trans Veh Technol 71(4):3429\u20133442","journal-title":"IEEE Trans Veh Technol"},{"issue":"7","key":"10090_CR3","doi-asserted-by":"publisher","first-page":"2074","DOI":"10.3390\/s18072074","volume":"18","author":"L Shu","year":"2018","unstructured":"Shu L et al (2018) A review of emotion recognition using physiological signals. Sensors 18(7):2074","journal-title":"Sensors"},{"key":"10090_CR7","doi-asserted-by":"crossref","unstructured":"Tushar AK, Kabir MA, Ahmed SI (2021) Mental health and sensing. Signal Processing Techniques for Computational Health Informatics, pp 247\u2013260","DOI":"10.1007\/978-3-030-54932-9_11"},{"key":"10090_CR24","doi-asserted-by":"publisher","first-page":"108581","DOI":"10.1109\/ACCESS.2019.2933048","volume":"7","author":"P Valdiviezo-Diaz","year":"2019","unstructured":"Valdiviezo-Diaz P et al (2019) A collaborative filtering approach based on Na\u00efve Bayes classifier. IEEE Access 7:108581\u2013108592","journal-title":"IEEE Access"},{"issue":"2","key":"10090_CR19","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1109\/T-AFFC.2011.30","volume":"3","author":"G Valenza","year":"2011","unstructured":"Valenza G, Lanata A, Scilingo EP (2011) The role of nonlinear dynamics in affective valence and arousal recognition. IEEE Trans Affect Comput 3(2):237\u2013249","journal-title":"IEEE Trans Affect Comput"},{"key":"10090_CR16","doi-asserted-by":"publisher","first-page":"1679","DOI":"10.3389\/fpsyg.2018.01679","volume":"9","author":"S Wallot","year":"2018","unstructured":"Wallot S, M\u00f8nster D (2018) Calculation of average mutual information (AMI) and false-nearest neighbors (FNN) for the estimation of embedding parameters of multidimensional time series in matlab. Front Psychol 9:1679","journal-title":"Front Psychol"},{"key":"10090_CR12","unstructured":"Yan Y et al (2022) Topological EEG nonlinear dynamics analysis for emotion recognition. IEEE Transactions on Cognitive and Developmental Systems"},{"key":"10090_CR15","doi-asserted-by":"crossref","unstructured":"Ziaratnia S et al (2023) Comparison of Physiological Responses to Stroop Word Color Test and IAPS Stimulation. in International Conference on Human-Computer Interaction. Springer","DOI":"10.1007\/978-3-031-35748-0_15"}],"container-title":["Cognitive Neurodynamics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11571-024-10090-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11571-024-10090-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11571-024-10090-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T15:14:54Z","timestamp":1731597294000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11571-024-10090-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,6]]},"references-count":32,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["10090"],"URL":"https:\/\/doi.org\/10.1007\/s11571-024-10090-4","relation":{},"ISSN":["1871-4080","1871-4099"],"issn-type":[{"value":"1871-4080","type":"print"},{"value":"1871-4099","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,6]]},"assertion":[{"value":"25 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This study was approved by the Ethical Committee of Tehran University of Medical Sciences.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"All participants and their parents signed an informed consent to take part in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}}]}}