{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:47:34Z","timestamp":1772556454746,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2018,5,11]],"date-time":"2018-05-11T00:00:00Z","timestamp":1525996800000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2019,11]]},"DOI":"10.1007\/s00521-018-3531-0","type":"journal-article","created":{"date-parts":[[2018,5,11]],"date-time":"2018-05-11T09:09:23Z","timestamp":1526029763000},"page":"6925-6932","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":75,"title":["Motor imagery tasks-based EEG signals classification using tunable-Q wavelet transform"],"prefix":"10.1007","volume":"31","author":[{"given":"Sachin","family":"Taran","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Varun","family":"Bajaj","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,5,11]]},"reference":[{"issue":"3","key":"3531_CR1","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1016\/S1388-2457(02)00387-5","volume":"114","author":"C Neuper","year":"2003","unstructured":"Neuper C, M\u00fcller GR, K\u00fcbler A, Birbaumer N, Pfurtscheller G (2003) Clinical application of an EEG-based brain\u2013computer interface: a case study in a patient with severe motor impairment. Clin Neurophysiol 114(3):399\u2013409","journal-title":"Clin Neurophysiol"},{"issue":"2","key":"3531_CR2","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1109\/TNSRE.2006.875546","volume":"14","author":"L Kauhanen","year":"2006","unstructured":"Kauhanen L, Nykopp T, Lehtonen J, Jylanki P, Heikkonen J, Rantanen P, Alaranta H, Sams M (2006) EEG and MEG brain\u2013computer interface for tetraplegic patients. IEEE Trans Neural Syst Rehabil Eng 14(2):190\u20133","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"5","key":"3531_CR3","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.1007\/s00521-012-1074-3","volume":"23","author":"A Ahangi","year":"2013","unstructured":"Ahangi A, Karamnejad M, Mohammadi N, Ebrahimpour R, Bagheri N (2013) Multiple classifier system for EEG signal classification with application to brain\u2013computer interfaces. Neural Comput Appl 23(5):1319\u201327","journal-title":"Neural Comput Appl"},{"issue":"136","key":"3531_CR4","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.cmpb.2016.08.013","volume":"1","author":"AR Hassan","year":"2016","unstructured":"Hassan AR, Subasi A (2016) Automatic identification of epileptic seizures from EEG signals using linear programming boosting. Comput Methods Programs Biomed 1(136):65\u201377","journal-title":"Comput Methods Programs Biomed"},{"key":"3531_CR5","doi-asserted-by":"crossref","unstructured":"Bashar SK, Hassan AR, Bhuiyan MI (2015) Motor imagery movements classification using multivariate EMD and short time Fourier transform. In: India conference (INDICON), annual IEEE, pp 1\u20136","DOI":"10.1109\/INDICON.2015.7443813"},{"issue":"31","key":"3531_CR6","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1016\/j.bspc.2016.09.007","volume":"1","author":"J Kevric","year":"2017","unstructured":"Kevric J, Subasi A (2017) Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system. Biomed Signal Process Control 1(31):398\u2013406","journal-title":"Biomed Signal Process Control"},{"key":"3531_CR7","doi-asserted-by":"crossref","unstructured":"Bashar SK, Hassan AR, Bhuiyan MI (2015) Identification of motor imagery movements from EEG signals using dual tree complex wavelet transform. In: Advances in computing, communications and informatics (ICACCI), IEEE international conference, pp 290\u2013296","DOI":"10.1109\/ICACCI.2015.7275623"},{"issue":"2","key":"3531_CR8","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.jneumeth.2008.09.014","volume":"176","author":"WY Hsu","year":"2009","unstructured":"Hsu WY, Sun YN (2009) EEG-based motor imagery analysis using weighted wavelet transform features. J Neurosci Methods 176(2):310\u20138","journal-title":"J Neurosci Methods"},{"issue":"116","key":"3531_CR9","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.measurement.2017.10.067","volume":"1","author":"S Taran","year":"2018","unstructured":"Taran S, Bajaj V, Sharma D, Siuly S, Sengur A (2018) Features based on analytic IMF for classifying motor imagery EEG signals in BCI applications. Measurement 1(116):68\u201376","journal-title":"Measurement"},{"issue":"4","key":"3531_CR10","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1109\/TNSRE.2012.2184838","volume":"20","author":"S Siuly","year":"2012","unstructured":"Siuly S, Li Y (2012) Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain\u2013computer interface. IEEE Trans Neural Syst Rehabil Eng 20(4):526\u201338","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"86","key":"3531_CR11","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.measurement.2016.02.059","volume":"1","author":"S Siuly","year":"2016","unstructured":"Siuly S, Wang H, Zhang Y (2016) Detection of motor imagery EEG signals employing Na\u00efve Bayes based learning process. Measurement 1(86):148\u201358","journal-title":"Measurement"},{"issue":"1","key":"3531_CR12","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/TNSRE.2004.841881","volume":"13","author":"DP Burke","year":"2005","unstructured":"Burke DP, Kelly SP, de Chazal P, Reilly RB, Finucane C (2005) A parametric feature extraction and classification strategy for brain\u2013computer interfacing. IEEE Trans Neural Syst Rehabil Eng 13(1):12\u20137","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"96","key":"3531_CR13","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.eswa.2017.12.015","volume":"15","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Wang Y, Zhou G, Jin J, Wang B, Wang X, Cichocki A (2018) Multi-kernel extreme learning machine for EEG classification in brain\u2013computer interfaces. Expert Syst Appl 15(96):302\u201310","journal-title":"Expert Syst Appl"},{"issue":"151","key":"3531_CR14","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.neucom.2014.09.078","volume":"3","author":"E Hortal","year":"2015","unstructured":"Hortal E, Planelles D, Costa A, I\u00e1\u00f1ez E, \u00dabeda A, Azor\u00edn JM, Fern\u00e1ndez E (2015) SVM-based Brain-Machine Interface for controlling a robot arm through four mental tasks. Neurocomputing 3(151):116\u201321","journal-title":"Neurocomputing"},{"issue":"2","key":"3531_CR15","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.cmpb.2014.02.018","volume":"116","author":"E Hortal","year":"2014","unstructured":"Hortal E, \u00dabeda A, I\u00e1nez E, Azorin JM (2014) Control of a 2 DoF robot using a brain\u2013machine interface. Comput Methods Programs Biomed 116(2):169\u201376","journal-title":"Comput Methods Programs Biomed"},{"key":"3531_CR16","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.robot.2015.05.010","volume":"72","author":"E Hortal","year":"2015","unstructured":"Hortal E, I\u00e1\u00f1ez E, \u00dabeda A, Perez-Vidal C, Azor\u00edn JM (2015) Combining a brain-machine interface and an electrooculography interface to perform pick and place tasks with a robotic arm. Robot Auton Syst 72:181\u2013188","journal-title":"Robot Auton Syst"},{"issue":"1","key":"3531_CR17","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/s11063-015-9469-7","volume":"44","author":"H Mo","year":"2016","unstructured":"Mo H, Zhao Y (2016) Motor imagery electroencephalograph classification based on optimized support vector machine by magnetic bacteria optimization algorithm. Neural Process Lett 44(1):185\u201397","journal-title":"Neural Process Lett"},{"issue":"9","key":"3531_CR18","doi-asserted-by":"publisher","first-page":"e74433","DOI":"10.1371\/journal.pone.0074433","volume":"8","author":"R Zhang","year":"2013","unstructured":"Zhang R, Xu P, Guo L, Zhang Y, Li P, Yao D (2013) Z-score linear discriminant analysis for EEG based brain\u2013computer interfaces. PLoS ONE 8(9):e74433","journal-title":"PLoS ONE"},{"issue":"41","key":"3531_CR19","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1016\/j.bspc.2017.11.014","volume":"31","author":"D Li","year":"2018","unstructured":"Li D, Zhang H, Khan MS, Mi F (2018) A self-adaptive frequency selection common spatial pattern and least squares twin support vector machine for motor imagery electroencephalography recognition. Biomed Signal Process Control 31(41):222\u201332","journal-title":"Biomed Signal Process Control"},{"issue":"12","key":"3531_CR20","doi-asserted-by":"publisher","first-page":"2936","DOI":"10.1109\/TBME.2010.2082540","volume":"57","author":"H Lu","year":"2010","unstructured":"Lu H, Eng HL, Guan C, Plataniotis KN, Venetsanopoulos AN (2010) Regularized common spatial pattern with aggregation for EEG classification in small-sample setting. IEEE Trans Biomed Eng 57(12):2936\u20132946","journal-title":"IEEE Trans Biomed Eng"},{"issue":"29","key":"3531_CR21","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.bspc.2016.05.009","volume":"1","author":"AR Hassan","year":"2016","unstructured":"Hassan AR (2016) Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting. Biomed Signal Process Control 1(29):22\u201330","journal-title":"Biomed Signal Process Control"},{"issue":"235","key":"3531_CR22","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.neucom.2016.12.062","volume":"26","author":"AR Hassan","year":"2017","unstructured":"Hassan AR, Haque MA (2017) An expert system for automated identification of obstructive sleep apnea from single-lead ECG using random under sampling boosting. Neurocomputing 26(235):122\u2013130","journal-title":"Neurocomputing"},{"issue":"271","key":"3531_CR23","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.jneumeth.2016.07.012","volume":"15","author":"AR Hassan","year":"2016","unstructured":"Hassan AR, Bhuiyan MI (2016) A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features. J Neurosci Methods 15(271):107\u201318","journal-title":"J Neurosci Methods"},{"issue":"128","key":"3531_CR24","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.knosys.2017.05.005","volume":"15","author":"AR Hassan","year":"2017","unstructured":"Hassan AR, Subasi A (2017) A decision support system for automated identification of sleep stages from single-channel EEG signals. Knowl Based Syst 15(128):115\u201324","journal-title":"Knowl Based Syst"},{"issue":"219","key":"3531_CR25","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.neucom.2016.09.011","volume":"5","author":"AR Hassan","year":"2017","unstructured":"Hassan AR, Bhuiyan MI (2017) An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting. Neurocomputing 5(219):76\u201387","journal-title":"Neurocomputing"},{"issue":"137","key":"3531_CR26","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.cmpb.2016.09.008","volume":"1","author":"AR Hassan","year":"2016","unstructured":"Hassan AR, Siuly S, Zhang Y (2016) Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating. Comput Methods Programs Biomed 1(137):247\u201359","journal-title":"Comput Methods Programs Biomed"},{"key":"3531_CR27","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.bspc.2017.01.001","volume":"34","author":"S Patidar","year":"2017","unstructured":"Patidar S, Panigrahi T (2017) Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals. Biomed Signal Process Control 34:74\u201380","journal-title":"Biomed Signal Process Control"},{"key":"3531_CR28","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.ymssp.2016.10.013","volume":"86","author":"Y Li","year":"2017","unstructured":"Li Y, Liang X, Xu M, Huang W (2017) Early fault feature extraction of rolling bearing based on ICD and tunable Q-factor wavelet transform. Mech Syst Signal Process 86:204\u201323","journal-title":"Mech Syst Signal Process"},{"issue":"8","key":"3531_CR29","doi-asserted-by":"publisher","first-page":"3560","DOI":"10.1109\/TSP.2011.2143711","volume":"59","author":"IW Selesnick","year":"2011","unstructured":"Selesnick IW (2011) Wavelet transform with tunable Q-factor. IEEE Trans Signal Process 59(8):3560\u20133575","journal-title":"IEEE Trans Signal Process"},{"issue":"2","key":"3531_CR30","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1109\/TNSRE.2006.875642","volume":"14","author":"B Blankertz","year":"2006","unstructured":"Blankertz B, Muller KR, Krusienski DJ, Schalk G, Wolpaw JR, Schlogl A, Pfurtscheller G, Millan JR, Schroder M, Birbaumer N (2006) The BCI competition III: validating alternative approaches to actual BCI problems. IEEE Trans Neural Syst Rehabil Eng 14(2):153\u2013159","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"6","key":"3531_CR31","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1109\/TBME.2004.827088","volume":"51","author":"G Dornhege","year":"2004","unstructured":"Dornhege G, Blankertz B, Curio G, Muller KR (2004) Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms. IEEE Trans Biomed Eng 51(6):993\u20131002","journal-title":"IEEE Trans Biomed Eng"},{"key":"3531_CR32","doi-asserted-by":"publisher","first-page":"14797","DOI":"10.1109\/ACCESS.2017.2724555","volume":"5","author":"RM Mehmood","year":"2017","unstructured":"Mehmood RM, Du R, Lee HJ (2017) Optimal feature selection and deep learning ensembles method for emotion recognition from human brain EEG sensors. IEEE Access 5:14797\u2013806","journal-title":"IEEE Access"},{"issue":"7","key":"3531_CR33","doi-asserted-by":"publisher","first-page":"16225","DOI":"10.3390\/s150716225","volume":"15","author":"X Wang","year":"2015","unstructured":"Wang X, Zheng Y, Zhao Z, Wang J (2015) Bearing fault diagnosis based on statistical locally linear embedding. Sensors 15(7):16225\u201347","journal-title":"Sensors"},{"issue":"1","key":"3531_CR34","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/s13755-017-0028-7","volume":"5","author":"S Taran","year":"2017","unstructured":"Taran S, Bajaj V, Siuly S (2017) An optimum allocation sampling based feature extraction scheme for distinguishing seizure and seizure-free EEG signals. Health Inf Sci Syst 5(1):7","journal-title":"Health Inf Sci Syst"},{"issue":"3","key":"3531_CR35","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1023\/A:1018628609742","volume":"9","author":"JA Suykens","year":"1999","unstructured":"Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293\u2013300","journal-title":"Neural Process Lett"},{"key":"3531_CR36","doi-asserted-by":"crossref","unstructured":"Suykens JA, Lukas L, Vandewalle J (2000) Sparse approximation using least squares support vector machines. In: The IEEE international symposium on circuits and systems, proceedings, ISCAS 2000 Geneva, vol 2 pp 757\u2013760","DOI":"10.1109\/ISCAS.2000.856439"},{"issue":"9","key":"3531_CR37","doi-asserted-by":"publisher","first-page":"10751","DOI":"10.1016\/j.eswa.2011.01.087","volume":"38","author":"M Zavar","year":"2011","unstructured":"Zavar M, Rahati S, Akbarzadeh-T MR, Ghasemifard H (2011) Evolutionary model selection in a wavelet-based support vector machine for automated seizure detection. Expert Syst Appl 38(9):10751\u201310758","journal-title":"Expert Syst Appl"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3531-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-018-3531-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3531-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,24]],"date-time":"2019-10-24T20:27:08Z","timestamp":1571948828000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-018-3531-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,11]]},"references-count":37,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2019,11]]}},"alternative-id":["3531"],"URL":"https:\/\/doi.org\/10.1007\/s00521-018-3531-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,11]]},"assertion":[{"value":"14 February 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}