{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T03:05:16Z","timestamp":1775012716541,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2018,9,18]],"date-time":"2018-09-18T00:00:00Z","timestamp":1537228800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Health Inf Sci Syst"],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1007\/s13755-018-0048-y","type":"journal-article","created":{"date-parts":[[2018,9,18]],"date-time":"2018-09-18T14:28:50Z","timestamp":1537280930000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Emotion classification using flexible analytic wavelet transform for electroencephalogram signals"],"prefix":"10.1007","volume":"6","author":[{"given":"Varun","family":"Bajaj","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sachin","family":"Taran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdulkadir","family":"Sengur","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,9,18]]},"reference":[{"issue":"1","key":"48_CR1","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/S0304-3940(97)00232-2","volume":"226","author":"LI Aftanas","year":"1997","unstructured":"Aftanas LI, Lotova NV, Koshkarov VI, Pokrovskaja VL, Popov SA, Makhnev VP. Non-linear analysis of emotion EEG: calculation of Kolmogorov entropy and the principal Lyapunov exponent. Neurosci Lett. 1997;226(1):13\u20136.","journal-title":"Neurosci Lett."},{"issue":"4","key":"48_CR2","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1088\/1741-2560\/1\/4\/004","volume":"1","author":"R Boostani","year":"2004","unstructured":"Boostani R, Moradi MH. A new approach in the BCI research based on fractal dimension as feature and Adaboost as classifier. J Neural Eng. 2004;1(4):212.","journal-title":"J Neural Eng."},{"issue":"3","key":"48_CR3","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1109\/TITB.2010.2041553","volume":"14","author":"CA Frantzidis","year":"2010","unstructured":"Frantzidis CA, Bratsas C, Papadelis CL, Konstantinidis E, Pappas C, Bamidis PD. Toward emotion aware computing: an integrated approach using multichannel neurophysiological recordings and affective visual stimuli. IEEE Trans Informn Technol Biomed. 2010;14(3):589\u201397.","journal-title":"IEEE Trans Informn Technol Biomed."},{"issue":"2","key":"48_CR4","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1109\/TITB.2009.2034649","volume":"14","author":"PC Petrantonakis","year":"2010","unstructured":"Petrantonakis PC, Hadjileontiadis LJ. Emotion recognition from EEG using higher order crossings. IEEE Trans Inform Technol Biomed. 2010;14(2):186\u201397.","journal-title":"IEEE Trans Inform Technol Biomed."},{"key":"48_CR5","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1007\/978-3-642-24955-6_87","volume-title":"Neural Information Processing","author":"Xiao-Wei Wang","year":"2011","unstructured":"Wang XW, Nie D, Lu BL. EEG-based emotion recognition using frequency domain features and support vector machines. In: International Conference on Neural Information Processing. Berlin: Springer; 2011. pp. 734\u201343."},{"issue":"8","key":"48_CR6","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1016\/j.ijhcs.2009.03.005","volume":"67","author":"G Chanel","year":"2009","unstructured":"Chanel G, Kierkels JJ, Soleymani M, Pun T. Short-term emotion assessment in a recall paradigm. Int J Hum-Comput Stud. 2009;67(8):607\u201327.","journal-title":"Int J Hum-Comput Stud."},{"issue":"7","key":"48_CR7","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TBME.2010.2048568","volume":"57","author":"YP Lin","year":"2010","unstructured":"Lin YP, Wang CH, Jung TP, Wu TL, Jeng SK, Duann JR, Chen JH. EEG-based emotion recognition in music listening. IEEE Trans Biomed Eng. 2010;57(7):1798\u2013806.","journal-title":"IEEE Trans Biomed Eng"},{"issue":"12","key":"48_CR8","doi-asserted-by":"publisher","first-page":"3498","DOI":"10.1109\/TBME.2012.2217495","volume":"59","author":"SK Hadjidimitriou","year":"2012","unstructured":"Hadjidimitriou SK, Hadjileontiadis LJ. Toward an EEG-based recognition of music liking using time-frequency analysis. IEEE Trans Biomed Eng. 2012;59(12):3498\u2013510.","journal-title":"IEEE Trans Biomed Eng."},{"issue":"1","key":"48_CR9","doi-asserted-by":"publisher","first-page":"45","DOI":"10.5405\/jmbe.710","volume":"31","author":"M Murugappan","year":"2011","unstructured":"Murugappan M, Nagarajan R, Yaacob S. Combining spatial filtering and wavelet transform for classifying human emotions using EEG signals. J Med Biol Eng. 2011;31(1):45\u201351.","journal-title":"J Med Biol Eng."},{"key":"48_CR10","first-page":"215","volume-title":"Brain-Computer Interfaces","author":"Varun Bajaj","year":"2014","unstructured":"Bajaj V, Pachori RB. Detection of human emotions using features based on the multiwavelet transform of EEG signals. In: Brain-Computer Interfaces. Cham: Springer; 2015. pp. 215\u201340."},{"key":"48_CR11","doi-asserted-by":"crossref","unstructured":"Bajaj V, Pachori RB. Human emotion classification from EEG signals using multiwavelet transform. In: Medical Biometrics, 2014 International Conference on, IEEE; 2014. pp. 125\u201330.","DOI":"10.1109\/ICMB.2014.29"},{"key":"48_CR12","doi-asserted-by":"crossref","unstructured":"Murugappan M. Human emotion classification using wavelet transform and KNN. In: Pattern Analysis and Intelligent Robotics (ICPAIR), 2011 International Conference on, 1, IEEE; 2011. pp. 148\u201353.","DOI":"10.1109\/ICPAIR.2011.5976886"},{"issue":"2","key":"48_CR13","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1109\/T-AFFC.2010.7","volume":"1","author":"PC Petrantonakis","year":"2010","unstructured":"Petrantonakis PC, Hadjileontiadis LJ. Emotion recognition from brain signals using hybrid adaptive filtering and higher order crossings analysis. IEEE Trans Affect Comput. 2010;1(2):81\u201397.","journal-title":"IEEE Trans Affect Comput."},{"key":"48_CR14","doi-asserted-by":"crossref","unstructured":"Liu Y, Sourina O, Nguyen MK. Real-time EEG-based human emotion recognition and visualization. In: Cyberworlds (CW), 2010 International Conference on, IEEE; 2010. pp. 262\u20139.","DOI":"10.1109\/CW.2010.37"},{"issue":"03","key":"48_CR15","doi-asserted-by":"publisher","first-page":"75","DOI":"10.4236\/jcc.2017.53009","volume":"5","author":"AQX Ang","year":"2017","unstructured":"Ang AQX, Yeong YQ, Ser W. Emotion classification from EEG signals using time-frequency-DWT features and ANN. J Comput Commun. 2017;5(03):75.","journal-title":"J Comput Commun."},{"issue":"2","key":"48_CR16","first-page":"150","volume":"8","author":"M Singh","year":"2015","unstructured":"Singh M, Singh M, Goyal M. Emotion classification using EEG entropy. Int J Inform Technol Knowl Manag. 2015;8(2):150\u20138.","journal-title":"Int J Inform Technol Knowl Manag."},{"issue":"1","key":"48_CR17","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1504\/IJAACS.2013.050696","volume":"6","author":"M Mikhail","year":"2013","unstructured":"Mikhail M, El-Ayat K, Coan JA, Allen JJ. Using minimal number of electrodes for emotion detection using brain signals produced from a new elicitation technique. Int J Auton Adapt Commun Syst. 2013;6(1):80\u201397.","journal-title":"Int J Auton Adapt Commun Syst."},{"key":"48_CR18","doi-asserted-by":"crossref","unstructured":"Khosrowabadi R, Quek HC, Wahab A, Ang KK. EEG-based emotion recognition using self-organizing map for boundary detection. In: Pattern Recognition (ICPR), 2010 20th International Conference on, IEEE; 2010. pp. 4242\u201345.","DOI":"10.1109\/ICPR.2010.1031"},{"key":"48_CR19","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.chb.2016.08.029","volume":"65","author":"AM Bhatti","year":"2016","unstructured":"Bhatti AM, Majid M, Anwar SM, Khan B. Human emotion recognition and analysis in response to audio music using brain signals. Comput Hum Behav. 2016;65:267\u201375.","journal-title":"Comput Hum Behav."},{"key":"48_CR20","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1016\/j.compeleceng.2016.04.009","volume":"53","author":"RM Mehmood","year":"2016","unstructured":"Mehmood RM, Lee HJ. A novel feature extraction method based on late positive potential for emotion recognition in human brain signal patterns. Comput Electr Eng. 2016;53:444\u201357.","journal-title":"Comput Electr Eng."},{"key":"48_CR21","doi-asserted-by":"crossref","unstructured":"Fan M, Chou CA. Recognizing affective state patterns using regularized learning with nonlinear dynamical features of EEG. In: Biomedical and Health Informatics (BHI), 2018 IEEE EMBS International Conference on, IEEE; 2018. pp. 137\u201340.","DOI":"10.1109\/BHI.2018.8333388"},{"issue":"1","key":"48_CR22","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/JBHI.2017.2688239","volume":"22","author":"S Katsigiannis","year":"2018","unstructured":"Katsigiannis S, Ramzan N. Dreamer: a database for emotion recognition through eeg and ecg signals from wireless low-cost off-the-shelf devices. IEEE J Biomed Health Inform. 2018;22(1):98\u2013107.","journal-title":"IEEE J Biomed Health Inform."},{"key":"48_CR23","unstructured":"Hu B, Li X, Sun S, Ratcliffe M. Attention recognition in EEG-based affective learning research using CFS+ KNN algorithm. IEEE\/ACM Transactions on Computational Biology and Bioinformatics; 2016."},{"key":"48_CR24","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.bspc.2018.01.015","volume":"42","author":"G Balasubramanian","year":"2018","unstructured":"Balasubramanian G, Kanagasabai A, Mohan J, Seshadri NG. Music induced emotion using wavelet packet decompositionAn EEG study. Biomed Signal Process Control. 2018;42:115\u201328.","journal-title":"Biomed Signal Process Control"},{"key":"48_CR25","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.eswa.2017.09.062","volume":"93","author":"B Nakisa","year":"2018","unstructured":"Nakisa B, Rastgoo MN, Tjondronegoro D, Chandran V. Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors. Expert Syst Appl. 2018;93:143\u201355.","journal-title":"Expert Syst Appl."},{"key":"48_CR26","first-page":"98","volume":"24","author":"DD Chakladar","year":"2018","unstructured":"Chakladar DD, Chakraborty S. EEG based emotion classification using correlation based subset selection. Biol Inspir Cognit Archit. 2018;24:98\u2013106.","journal-title":"Biol Inspir Cognit Archit."},{"issue":"3","key":"48_CR27","doi-asserted-by":"publisher","first-page":"773","DOI":"10.3758\/s13428-014-0500-0","volume":"47","author":"CA Gabert-Quillen","year":"2015","unstructured":"Gabert-Quillen CA, Bartolini EE, Abravanel BT, Sanislow CA. Ratings for emotion film clips. Behav Res Methods. 2015;47(3):773\u201387.","journal-title":"Behav Res Methods."},{"issue":"1","key":"48_CR28","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","volume":"3","author":"S Koelstra","year":"2012","unstructured":"Koelstra S, Muhl C, Soleymani M, Lee JS, Yazdani A, Ebrahimi T, Patras I. Deap: a database for emotion analysis; using physiological signals. IEEE Trans Affect Comput. 2012;3(1):18\u201331.","journal-title":"IEEE Trans Affect Comput."},{"issue":"1","key":"48_CR29","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/0005-7916(94)90063-9","volume":"25","author":"MM Bradley","year":"1994","unstructured":"Bradley MM, Lang PJ. Measuring emotion: the self-assessment manikin and the semantic differential. J Behav Therapy Exp Psychiatr. 1994;25(1):49\u201359.","journal-title":"J Behav Therapy Exp Psychiatr."},{"issue":"3","key":"48_CR30","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1504\/IJMEI.2009.022645","volume":"1","author":"M Murugappan","year":"2009","unstructured":"Murugappan M, Juhari MRBM, Nagarajan R, Yaacob S. An investigation on visual and audiovisual stimulus based emotion recognition using EEG. J. Med Eng Inform. 2009;1(3):342\u201356.","journal-title":"J. Med Eng Inform."},{"issue":"4","key":"48_CR31","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1159\/000077154","volume":"17","author":"HJ Rosen","year":"2004","unstructured":"Rosen HJ, Pace-Savitsky K, Perry RJ, Kramer JH, Miller BL, Levenson RW. Recognition of emotion in the frontal and temporal variants of frontotemporal dementia. Dement Geriatr Cognit Disord. 2004;17(4):277\u201381.","journal-title":"Dement Geriatr Cognit Disord."},{"issue":"5","key":"48_CR32","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.1109\/TSP.2012.2232655","volume":"61","author":"I Bayram","year":"2013","unstructured":"Bayram I. An analytic wavelet transform with a flexible time-frequency covering. IEEE Trans Signal Process. 2013;61(5):1131\u201342.","journal-title":"IEEE Trans Signal Process."},{"key":"48_CR33","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.ymssp.2015.03.030","volume":"64","author":"C Zhang","year":"2015","unstructured":"Zhang C, Li B, Chen B, Cao H, Zi Y, He Z. Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform. Mech Syst Signal Process. 2015;64:162\u201387.","journal-title":"Mech Syst Signal Process."},{"key":"48_CR34","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.bspc.2016.07.003","volume":"31","author":"UR Acharya","year":"2017","unstructured":"Acharya UR, Sudarshan VK, Koh JE, Martis RJ, Tan JH, Oh SL, Chua CK. Application of higher-order spectra for the characterization of coronary artery disease using electrocardiogram signals. Biomed Signal Process Control. 2017;31:31\u201343.","journal-title":"Biomed Signal Process Control."},{"issue":"1","key":"48_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13755-017-0020-2","volume":"5","author":"S Taran","year":"2017","unstructured":"Taran S, Bajaj V, Siuly S. An optimum allocation sampling based feature extraction scheme for distinguishing seizure and seizure-free EEG signals. Health Inform Sci Syst. 2017;5(1):1\u20137.","journal-title":"Health Inform Sci Syst."},{"issue":"3","key":"48_CR36","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1049\/iet-smt.2017.0232","volume":"12","author":"S Taran","year":"2018","unstructured":"Taran S, Bajaj V. Rhythm based identification of alcohol EEG signals. IET Sci Meas Technol. 2018;12(3):343\u20139.","journal-title":"IET Sci Meas Technol."},{"key":"48_CR37","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3531-0","author":"S Taran","year":"2018","unstructured":"Taran S, Bajaj V. Motor imagery tasks-based EEG signals classification using tunable-Q wavelet transform. Neural Comput Appl. (2018). \n                    https:\/\/doi.org\/10.1007\/s00521-018-3531-0","journal-title":"INeural Comput Appl."},{"key":"48_CR38","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.measurement.2017.10.067","volume":"116","author":"S Taran","year":"2018","unstructured":"Taran S, Bajaj V, Sharma D, Siuly S, Sengur A. Features based on analytic IMF for classifying motor imagery EEG signals in BCI applications. Measurement. 2018;116:68\u201376.","journal-title":"Measurement."},{"issue":"3","key":"48_CR39","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1016\/j.cap.2010.11.051","volume":"11","author":"KS Kim","year":"2011","unstructured":"Kim KS, Choi HH, Moon CS, Mun CW. Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions. Curr Appl Phys. 2011;11(3):740\u20135.","journal-title":"Curr Appl Phys."},{"key":"48_CR40","first-page":"245","volume-title":"Information and Communication Technology for Sustainable Development","author":"Jenifer Mariam Johnson","year":"2017","unstructured":"Johnson JM, Yadav A. Fault detection and classification technique for HVDC transmission lines using KNN. In: Information and Communication Technology for Sustainable Development. Singapore: Springer; 2018. pp. 245\u201353."}],"container-title":["Health Information Science and Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13755-018-0048-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-018-0048-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-018-0048-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,17]],"date-time":"2019-09-17T23:23:00Z","timestamp":1568762580000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13755-018-0048-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,18]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,12]]}},"alternative-id":["48"],"URL":"https:\/\/doi.org\/10.1007\/s13755-018-0048-y","relation":{},"ISSN":["2047-2501"],"issn-type":[{"value":"2047-2501","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,18]]},"assertion":[{"value":"29 May 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"12"}}