{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T17:24:48Z","timestamp":1777742688325,"version":"3.51.4"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,3,11]],"date-time":"2020-03-11T00:00:00Z","timestamp":1583884800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,3,11]],"date-time":"2020-03-11T00:00:00Z","timestamp":1583884800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100008536","name":"Amazon Web Services","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008536","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Emotion recognition using brain signals has the potential to change the way we identify and treat some health conditions. Difficulties and limitations may arise in general emotion recognition software due to the restricted number of facial expression triggers, dissembling of emotions, or among people with alexithymia. Such triggers are identified by studying the continuous brainwaves generated by human brain. Electroencephalogram (EEG) signals from the brain give us a more diverse insight on emotional states that one may not be able to express. Brainwave EEG signals can reflect the changes in electrical potential resulting from communications networks between neurons. This research involves analyzing the epoch data from EEG sensor channels and performing comparative analysis of multiple machine learning techniques [namely Support Vector Machine (SVM), K-nearest neighbor, Linear Discriminant Analysis, Logistic Regression and Decision Trees each of these models] were tested with and without principal component analysis (PCA) for dimensionality reduction. Grid search was also utilized for hyper-parameter tuning for each of the tested machine learning models over Spark cluster for lowered execution time. The DEAP Dataset was used in this study, which is a multimodal dataset for the analysis of human affective states. The predictions were based on the labels given by the participants for each of the 40 1-min long excerpts of music. music. Participants rated each video in terms of the level of arousal, valence, like\/dislike, dominance and familiarity. The binary class classifiers were trained on the time segmented, 15 s intervals of epoch data, individually for each of the 4 classes. PCA with SVM performed the best and produced an F1-score of 84.73% with 98.01% recall in the 30th to 45th interval of segmentation. For each of the time segments and \u201ca binary training class\u201d a different classification model converges to a better accuracy and recall than others. The results prove that different classification models must be used to identify different emotional states.<\/jats:p>","DOI":"10.1186\/s40537-020-00289-7","type":"journal-article","created":{"date-parts":[[2020,3,10]],"date-time":"2020-03-10T21:04:01Z","timestamp":1583874241000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":137,"title":["A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals"],"prefix":"10.1186","volume":"7","author":[{"given":"Vikrant","family":"Doma","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6255-4741","authenticated-orcid":false,"given":"Matin","family":"Pirouz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,3,11]]},"reference":[{"key":"289_CR1","doi-asserted-by":"publisher","first-page":"1953","DOI":"10.1017\/S0033291712002607","volume":"43","author":"A Daros","year":"2013","unstructured":"Daros A, Zakzanis K, Ruocco A. Facial emotion recognition in borderline personality disorder. Psychol Med. 2013;43:1953\u201363.","journal-title":"Psychol Med"},{"key":"289_CR2","doi-asserted-by":"crossref","unstructured":"Schaaff K, Schultz T. Towards emotion recognition from electroencephalographic signals. In: 2009 3rd international conference on affective computing and intelligent interaction and workshops. New York: IEEE; 2009. p. 1\u20136.","DOI":"10.1109\/ACII.2009.5349316"},{"key":"289_CR3","doi-asserted-by":"crossref","unstructured":"Bertsimas D, Dunn J, Paschalidis A. Regression and classification using optimal decision trees. In: 2017 IEEE MIT undergraduate research technology conference (URTC). 2017. p. 1\u20134.","DOI":"10.1109\/URTC.2017.8284195"},{"key":"289_CR4","doi-asserted-by":"crossref","unstructured":"Jiahui Pan, Yuanqing Li, Jun Wang. An EEG-based brain\u2013computer interface for emotion recognition. In: 2016 international joint conference on neural networks (IJCNN). 2016. p. 2063\u201367.","DOI":"10.1109\/IJCNN.2016.7727453"},{"key":"289_CR5","doi-asserted-by":"publisher","first-page":"1424","DOI":"10.1109\/34.895976","volume":"22","author":"M Pantic","year":"2000","unstructured":"Pantic M, Rothkrantz LJ. Automatic analysis of facial expressions: the state of the art. IEEE Trans Pattern Anal Mach Intell. 2000;22:1424\u201345.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"289_CR6","doi-asserted-by":"publisher","first-page":"128","DOI":"10.14445\/22312803\/IJCTT-V48P126","volume":"48","author":"F Osisanwo","year":"2017","unstructured":"Osisanwo F, et al. Supervised machine learning algorithms: classification and comparison. Int J Comput Trends Technol. 2017;48:128\u201338.","journal-title":"Int J Comput Trends Technol"},{"key":"289_CR7","first-page":"184","volume":"5","author":"SRN Kalhori","year":"2013","unstructured":"Kalhori SRN, Zeng X-J. Evaluation and comparison of different machine learning methods to predict outcome of tuberculosis treatment course. J Intell Learn Syst Appl. 2013;5:184.","journal-title":"J Intell Learn Syst Appl"},{"key":"289_CR8","unstructured":"Vanitha V, Krishnan P. Real time stress detection system based on EEG signals. 2016."},{"key":"289_CR9","doi-asserted-by":"crossref","unstructured":"Liao C-Y, Chen R-C, Tai S-K. Emotion stress detection using eeg signal and deep learning technologies. In: 2018 IEEE international conference on applied system invention (ICASI). New York: IEEE; 2018. p. 90\u20133.","DOI":"10.1109\/ICASI.2018.8394414"},{"key":"289_CR10","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. Deap: a database for emotion analysis; using physiological signals. IEEE Trans Affect Comput. 2011;3:18\u201331.","journal-title":"IEEE Trans Affect Comput"},{"key":"289_CR11","unstructured":"Jia W et\u00a0al. Electroencephalography (eeg)-based instinctive brain-control of a quadruped locomotion robot. In: 2012 annual international conference of the IEEE engineering in medicine and biology society. New York: IEEE; 2012. p. 1777\u201381."},{"key":"289_CR12","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.procs.2015.12.140","volume":"72","author":"MN Fakhruzzaman","year":"2015","unstructured":"Fakhruzzaman MN, Riksakomara E, Suryotrisongko H. Eeg wave identification in human brain with emotiv epoc for motor imagery. Procedia Comput Sci. 2015;72:269\u201376.","journal-title":"Procedia Comput Sci"},{"key":"289_CR13","doi-asserted-by":"crossref","unstructured":"Shariat S, Pavlovic V, Papathomas T, Braun A, Sinha P. Sparse dictionary methods for EEG signal classification in face perception. In: 2010 IEEE international workshop on machine learning for signal processing. New York: IEEE; 2010. p. 331\u20136.","DOI":"10.1109\/MLSP.2010.5589166"},{"key":"289_CR14","doi-asserted-by":"publisher","first-page":"016003","DOI":"10.1088\/1741-2560\/14\/1\/016003","volume":"14","author":"YR Tabar","year":"2016","unstructured":"Tabar YR, Halici U. A novel deep learning approach for classification of EEG motor imagery signals. J Neural Eng. 2016;14:016003.","journal-title":"J Neural Eng"},{"key":"289_CR15","doi-asserted-by":"crossref","unstructured":"Chambon S, Thorey V, Arnal PJ, Mignot E, Gramfort A. A deep learning architecture to detect events in EEG signals during sleep. In: 2018 IEEE 28th international workshop on machine learning for signal processing (MLSP). New York: IEEE; 2018. p. 1\u20136.","DOI":"10.1109\/MLSP.2018.8517067"},{"key":"289_CR16","unstructured":"Bashivan P, Rish I, Yeasin M, Codella N. Learning representations from EEG with deep recurrent-convolutional neural networks. arXiv preprint arXiv:1511.06448. 2015."},{"key":"289_CR17","doi-asserted-by":"crossref","unstructured":"Thomas J, et\u00a0al. EEG classification via convolutional neural network-based interictal epileptiform event detection. In: 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC). New York: IEEE; 2018. p. 3148\u201351.","DOI":"10.1109\/EMBC.2018.8512930"},{"key":"289_CR18","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.neuroimage.2019.05.026","volume":"198","author":"L Pion-Tonachini","year":"2019","unstructured":"Pion-Tonachini L, Kreutz-Delgado K, Makeig S. Iclabel: an automated electroencephalographic independent component classifier, dataset, and website. NeuroImage. 2019;198:181\u201397.","journal-title":"NeuroImage"},{"key":"289_CR19","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1002\/acn3.50817","volume":"67","author":"AF Struck","year":"2019","unstructured":"Struck AF, et al. Comparison of machine learning models for seizure prediction in hospitalized patients. Ann Clin Transl Neurol. 2019;67:1239\u201347.","journal-title":"Ann Clin Transl Neurol"},{"key":"289_CR20","doi-asserted-by":"crossref","unstructured":"Belakhdar I, Kaaniche W, Djmel R, Ouni B. A comparison between ANN and SVM classifier for drowsiness detection based on single EEG channel. 2016. p. 443\u20136.","DOI":"10.1109\/ATSIP.2016.7523132"},{"key":"289_CR21","doi-asserted-by":"crossref","unstructured":"Li S, Feng H. EEG signal classification method based on feature priority analysis and CNN. 2019. p. 403\u20136.","DOI":"10.1109\/CISCE.2019.00095"},{"key":"289_CR22","unstructured":"Zhiwei L, Minfen S. Classification of mental task EEG signals using wavelet packet entropy and SVM. 2007. p. 906\u20139."},{"key":"289_CR23","doi-asserted-by":"crossref","unstructured":"Jin J, Wang X, Wang B. Classification of direction perception EEG based on PCA-SVM, vol. 2. 2007. p. 116\u201320.","DOI":"10.1109\/ICNC.2007.298"},{"key":"289_CR24","doi-asserted-by":"crossref","unstructured":"Song F, Guo Z, Mei D. Feature selection using principal component analysis. In: 2010 international conference on system science, engineering design and manufacturing informatization, vol.\u00a01. 2010. p. 27\u201330.","DOI":"10.1109\/ICSEM.2010.14"},{"key":"289_CR25","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"},{"key":"289_CR26","doi-asserted-by":"crossref","unstructured":"Rajaguru H, Prabhakar SK. Non linear ica and logistic regression for classification of epilepsy from eeg signals. In: 2017 international conference of electronics, communication and aerospace technology (ICECA), vol.\u00a01. 2017. p. 577\u201380.","DOI":"10.1109\/ICECA.2017.8203602"},{"key":"289_CR27","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.procs.2018.10.392","volume":"143","author":"A Bablani","year":"2018","unstructured":"Bablani A, Edla DR, Dodia S. Classification of EEG data using k-nearest neighbor approach for concealed information test. Procedia Comput Sci. 2018;143:242\u20139.","journal-title":"Procedia Comput Sci"},{"key":"289_CR28","doi-asserted-by":"crossref","unstructured":"Tripathi S, Acharya S, Sharma RD, Mittal S, Bhattacharya S. Using deep and convolutional neural networks for accurate emotion classification on deap dataset. In: Twenty-ninth IAAI conference. 2017.","DOI":"10.1609\/aaai.v31i2.19105"},{"key":"289_CR29","doi-asserted-by":"crossref","unstructured":"Placidi G, Di\u00a0Giamberardino P, Petracca A, Spezialetti M, Iacoviello D. Classification of emotional signals from the deap dataset. In: International congress on neurotechnology, electronics and informatics, vol.\u00a02. SCITEPRESS; 2016. p. 15\u201321.","DOI":"10.5220\/0006043400150021"},{"key":"289_CR30","unstructured":"WeichenXu123 & mengxr. Spark-Sklearn repo. 2018. https:\/\/github.com\/databricks\/spark-sklearn."},{"key":"289_CR31","doi-asserted-by":"crossref","unstructured":"Oishi S, Kurtz JL. The positive psychology of positive emotions: an avuncular view. Designing positive psychology: taking stock and moving forward. 2011. p. 101\u201314.","DOI":"10.1093\/acprof:oso\/9780195373585.003.0007"},{"key":"289_CR32","unstructured":"Kort B, Reilly R, Picard RW. An affective model of interplay between emotions and learning: reengineering educational pedagogy-building a learning companion. In: Proceedings IEEE international conference on advanced learning technologies. New York: IEEE; 2001. p. 43\u20136."},{"key":"289_CR33","doi-asserted-by":"crossref","unstructured":"Liu W, Zheng W-L, Lu B-L. Emotion recognition using multimodal deep learning. In: International conference on neural information processing. Berlin: Springer; 2016. p. 521\u20139.","DOI":"10.1007\/978-3-319-46672-9_58"},{"key":"289_CR34","doi-asserted-by":"crossref","unstructured":"Dabas H, Sethi C, Dua C, Dalawat M, Sethia D. Emotion classification using EEG signals. In: Proceedings of the 2018 2nd international conference on computer science and artificial intelligence. ACM; 2018. p. 380\u20134.","DOI":"10.1145\/3297156.3297177"},{"key":"289_CR35","unstructured":"Song T, Zheng W, Song P, Cui Z. EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans Affect Comput. 2018."},{"key":"289_CR36","doi-asserted-by":"crossref","unstructured":"MacIntyre PD, Vincze L. Positive and negative emotions underlie motivation for l2 learning. Stud Second Lang Learn Teach. 2017;7.","DOI":"10.14746\/ssllt.2017.7.1.4"},{"key":"289_CR37","doi-asserted-by":"crossref","unstructured":"Shivhare SN, Khethawat S. Emotion detection from text. 2012. arXiv preprint arXiv:1205.4944.","DOI":"10.5121\/csit.2012.2237"},{"key":"289_CR38","doi-asserted-by":"crossref","unstructured":"Suwicha\u00a0Jirayucharoensak SP-N, Israsena P. EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation. Sci World J. 2014. Article ID 627892.","DOI":"10.1155\/2014\/627892"},{"key":"289_CR39","unstructured":"Zheng W-L, Zhu J-Y, Lu B-L. Identifying stable patterns over time for emotion recognition from EEG. IEEE Trans Affect Comput. 2017."}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-020-00289-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s40537-020-00289-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-020-00289-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T07:02:22Z","timestamp":1666076542000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-020-00289-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,11]]},"references-count":39,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["289"],"URL":"https:\/\/doi.org\/10.1186\/s40537-020-00289-7","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,11]]},"assertion":[{"value":"9 September 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"18"}}