{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:28:41Z","timestamp":1777422521637,"version":"3.51.4"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2020,10,10]],"date-time":"2020-10-10T00:00:00Z","timestamp":1602288000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,10,10]],"date-time":"2020-10-10T00:00:00Z","timestamp":1602288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s00521-020-05393-6","type":"journal-article","created":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T23:04:26Z","timestamp":1602284666000},"page":"6233-6246","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Mental imagery classification using one-dimensional convolutional neural network for target selection in single-channel BCI-controlled mobile robot"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0743-6328","authenticated-orcid":false,"given":"Tarmizi Ahmad","family":"Izzuddin","sequence":"first","affiliation":[]},{"given":"Norlaili Mat","family":"Safri","sequence":"additional","affiliation":[]},{"given":"Mohd Afzan","family":"Othman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,10]]},"reference":[{"key":"5393_CR1","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1016\/j.tics.2015.08.003","volume":"19","author":"J Pearson","year":"2015","unstructured":"Pearson J, Naselaris T, Holmes EA, Kosslyn SM (2015) Mental imagery: functional mechanisms and clinical applications. Trends Cogn Sci 19:590\u2013602. https:\/\/doi.org\/10.1016\/j.tics.2015.08.003","journal-title":"Trends Cogn Sci"},{"key":"5393_CR2","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1016\/S0278-2626(03)00036-8","volume":"51","author":"EA Curran","year":"2003","unstructured":"Curran EA, Stokes MJ (2003) Learning to control brain activity: a review of the production and control of EEG components for driving brain\u2013computer interface (BCI) systems. Brain Cogn 51:326\u2013336. https:\/\/doi.org\/10.1016\/S0278-2626(03)00036-8","journal-title":"Brain Cogn"},{"key":"5393_CR3","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1515\/JISYS.1997.7.1-2.165","volume":"7","author":"CW Anderson","year":"1997","unstructured":"Anderson CW (1997) Effects of variations in neural network topology and output averaging on the discrimination of mental tasks from spontaneous electroencephalogram. J Intell Syst 7:165\u2013190. https:\/\/doi.org\/10.1515\/JISYS.1997.7.1-2.165","journal-title":"J Intell Syst"},{"key":"5393_CR4","unstructured":"Mill\u00e1n JDR (2002) Brain\u2013computer interfaces. In: The handbook of brain theory and neural networks, 2nd edn. MIT Press"},{"key":"5393_CR5","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1109\/10.64464","volume":"37","author":"ZA Keirn","year":"1990","unstructured":"Keirn ZA, Aunon JI (1990) A new mode of communication between man and his surroundings. IEEE Trans Biomed Eng 37:1209\u20131214. https:\/\/doi.org\/10.1109\/10.64464","journal-title":"IEEE Trans Biomed Eng"},{"key":"5393_CR6","doi-asserted-by":"publisher","DOI":"10.1109\/86.847823","author":"JR Wolpaw","year":"2000","unstructured":"Wolpaw JR, McFarland DJ, Vaughan TM (2000) Brain\u2013computer interface research at the Wadsworth Center. IEEE Trans Rehabil Eng. https:\/\/doi.org\/10.1109\/86.847823","journal-title":"IEEE Trans Rehabil Eng"},{"key":"5393_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3820-7","author":"WG de Oliveira J\u00fanior","year":"2018","unstructured":"de Oliveira J\u00fanior WG, de Oliveira JM, Munoz R, de Albuquerque VHC (2018) A proposal for internet of smart home things based on BCI system to aid patients with amyotrophic lateral sclerosis. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-018-3820-7","journal-title":"Neural Comput Appl"},{"key":"5393_CR8","doi-asserted-by":"crossref","unstructured":"Barbosa AOG, Achanccaray DR, Meggiolaro MA (2010) Activation of a mobile robot through a brain computer interface. In: 2010 IEEE international conference on robotics and automation. IEEE, pp 4815\u20134821","DOI":"10.1109\/ROBOT.2010.5509150"},{"key":"5393_CR9","doi-asserted-by":"publisher","first-page":"2095","DOI":"10.14419\/ijet.v7i4.12843","volume":"7","author":"TA Izzuddin","year":"2018","unstructured":"Izzuddin TA, Safri NM, Zohedi FN, Othman MA, Hazim MSAS (2018) Single channel electroencephalogram (EEG) brain computer interface (BCI) feature extraction and quantisation method for support vector machine classification. Int J Eng Technol 7:2095\u20132099. https:\/\/doi.org\/10.14419\/ijet.v7i4.12843","journal-title":"Int J Eng Technol"},{"key":"5393_CR10","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.neucom.2014.09.078","volume":"151","author":"E Hortal","year":"2015","unstructured":"Hortal E, Planelles D, Costa A et al (2015) SVM-based brain\u2013machine interface for controlling a robot arm through four mental tasks. Neurocomputing 151:116\u2013121. https:\/\/doi.org\/10.1016\/j.neucom.2014.09.078","journal-title":"Neurocomputing"},{"key":"5393_CR11","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"5393_CR12","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.neunet.2014.08.005","volume":"64","author":"TN Sainath","year":"2015","unstructured":"Sainath TN, Kingsbury B, Saon G et al (2015) Deep convolutional neural networks for large-scale speech tasks. Neural Netw 64:39\u201348. https:\/\/doi.org\/10.1016\/j.neunet.2014.08.005","journal-title":"Neural Netw"},{"key":"5393_CR13","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/14\/1\/016003","author":"YR Tabar","year":"2017","unstructured":"Tabar YR, Halici U (2017) A novel deep learning approach for classification of EEG motor imagery signals. J Neural Eng. https:\/\/doi.org\/10.1088\/1741-2560\/14\/1\/016003","journal-title":"J Neural Eng"},{"key":"5393_CR14","doi-asserted-by":"publisher","first-page":"5391","DOI":"10.1002\/hbm.23730","volume":"38","author":"RT Schirrmeister","year":"2017","unstructured":"Schirrmeister RT, Springenberg JT, Fiederer LDJ et al (2017) Deep learning with convolutional neural networks for EEG decoding and visualisation. Hum Brain Mapp 38:5391\u20135420. https:\/\/doi.org\/10.1002\/hbm.23730","journal-title":"Hum Brain Mapp"},{"key":"5393_CR15","doi-asserted-by":"publisher","first-page":"056013","DOI":"10.1088\/1741-2552\/aace8c","volume":"15","author":"VJ Lawhern","year":"2018","unstructured":"Lawhern VJ, Solon AJ, Waytowich NR et al (2018) EEGNet: a compact convolutional neural network for EEG-based brain\u2013computer interfaces. J Neural Eng 15:056013. https:\/\/doi.org\/10.1088\/1741-2552\/aace8c","journal-title":"J Neural Eng"},{"key":"5393_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04367-7","author":"KH Cheah","year":"2019","unstructured":"Cheah KH, Nisar H, Yap VV, Lee CY (2019) Convolutional neural networks for classification of music-listening EEG: comparing 1D convolutional kernels with 2D kernels and cerebral laterality of musical influence. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-019-04367-7","journal-title":"Neural Comput Appl"},{"key":"5393_CR17","doi-asserted-by":"publisher","first-page":"7","DOI":"10.11113\/jt.v61.1628","volume":"61","author":"H Azmy","year":"2013","unstructured":"Azmy H, Safri NM (2013) EEG based BCI using visual imagery task for robot control. J Technol (Sci Eng) 61:7\u201311. https:\/\/doi.org\/10.11113\/jt.v61.1628","journal-title":"J Technol (Sci Eng)"},{"key":"5393_CR18","doi-asserted-by":"crossref","unstructured":"Burget F, Fiederer LDJ, Kuhner D et al (2017) Acting thoughts: towards a mobile robotic service assistant for users with limited communication skills. In: 2017 European conference on mobile robots (ECMR). IEEE, pp 1\u20136","DOI":"10.1109\/ECMR.2017.8098658"},{"key":"5393_CR19","doi-asserted-by":"publisher","first-page":"046030","DOI":"10.1088\/1741-2552\/aac577","volume":"15","author":"F Lotte","year":"2018","unstructured":"Lotte F, Jeunet C (2018) Defining and quantifying users\u2019 mental imagery-based BCI skills: a first step. J Neural Eng 15:046030. https:\/\/doi.org\/10.1088\/1741-2552\/aac577","journal-title":"J Neural Eng"},{"key":"5393_CR20","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1109\/TNN.2002.1000132","volume":"13","author":"JDR Mill\u00e1n","year":"2002","unstructured":"Mill\u00e1n JDR, Mouri\u00f1o J, Franz\u00e9 M et al (2002) A local neural classifier for the recognition of EEG patterns associated to mental tasks. IEEE Trans Neural Netw 13:678\u2013686. https:\/\/doi.org\/10.1109\/TNN.2002.1000132","journal-title":"IEEE Trans Neural Netw"},{"key":"5393_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0143962","volume":"10","author":"C Jeunet","year":"2015","unstructured":"Jeunet C, Nkaoua B, Subramanian S et al (2015) Predicting mental imagery-based BCI performance from personality, cognitive profile and neurophysiological patterns. PLoS ONE 10:1\u201321. https:\/\/doi.org\/10.1371\/journal.pone.0143962","journal-title":"PLoS ONE"},{"key":"5393_CR22","unstructured":"JeunetC, N\u2019Kaoua B, Lotte F (2017) Towards a cognitive model of MI-BCI user training. In: International Graz BCI conference. p hal-01519476"},{"key":"5393_CR23","doi-asserted-by":"publisher","unstructured":"Bashivan P, Rish I, Yeasin M, Codella N (2015) Learning representations from EEG with deep recurrent-convolutional neural networks. arXiv 1\u201315. https:\/\/doi.org\/10.1080\/03610928808829796","DOI":"10.1080\/03610928808829796"},{"key":"5393_CR24","doi-asserted-by":"crossref","unstructured":"Mousavi M, de Sa VR (2019) Temporally adaptive common spatial patterns with deep convolutional neural networks. In: 2019 41st Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 4533\u20134536","DOI":"10.1109\/EMBC.2019.8857423"},{"key":"5393_CR25","doi-asserted-by":"crossref","unstructured":"Izzuddin TA, Ariffin MA, Bohari ZH et al (2015) Movement intention detection using neural network for quadriplegic assistive machine. In: 2015 IEEE international conference on control system, computing and engineering (ICCSCE). IEEE, pp 275\u2013280","DOI":"10.1109\/ICCSCE.2015.7482197"},{"key":"5393_CR26","doi-asserted-by":"crossref","unstructured":"Rashid M, Sulaiman N, Mustafa M et al (2020) Investigating the possibility of brain actuated mobile robot through single-channel EEG headset. In: Lecture notes in electrical engineering, vol 632. Springer, Singapore, pp 579\u2013590","DOI":"10.1007\/978-981-15-2317-5_49"},{"key":"5393_CR27","doi-asserted-by":"crossref","unstructured":"Stephygraph LR, Arunkumar N, Venkatraman V (2015) Wireless mobile robot control through human machine interface using brain signals. In: 2015 International conference on smart technologies and management for computing, communication, controls, energy and materials (ICSTM). IEEE, pp 596\u2013603","DOI":"10.1109\/ICSTM.2015.7225484"},{"key":"5393_CR28","doi-asserted-by":"crossref","unstructured":"Ullah K, Ali M, Rizwan M, Imran M (2011) Low-cost single-channel EEG based communication system for people with lock-in syndrome. In: 2011 IEEE 14th international multitopic conference. IEEE, pp 120\u2013125","DOI":"10.1109\/INMIC.2011.6151455"},{"key":"5393_CR29","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2019.00250","author":"M Ogino","year":"2019","unstructured":"Ogino M, Kanoga S, Muto M, Mitsukura Y (2019) Analysis of prefrontal single-channel EEG data for portable auditory ERP-based brain\u2013computer interfaces. Front Hum Neurosci. https:\/\/doi.org\/10.3389\/fnhum.2019.00250","journal-title":"Front Hum Neurosci"},{"key":"5393_CR30","doi-asserted-by":"publisher","first-page":"18940","DOI":"10.1109\/ACCESS.2019.2895688","volume":"7","author":"SU Amin","year":"2019","unstructured":"Amin SU, Alsulaiman M, Muhammad G et al (2019) Multilevel weighted feature fusion using convolutional neural networks for EEG motor imagery classification. IEEE Access 7:18940\u201318950. https:\/\/doi.org\/10.1109\/ACCESS.2019.2895688","journal-title":"IEEE Access"},{"key":"5393_CR31","first-page":"8757","volume":"10","author":"MS Adha","year":"2015","unstructured":"Adha MS, Safri NM, Othman MA (2015) Real-time target selection based on electroencephalogram (EEG) signal. ARPN J Eng Appl Sci 10:8757\u20138761","journal-title":"ARPN J Eng Appl Sci"},{"key":"5393_CR32","doi-asserted-by":"crossref","unstructured":"Kiranyaz S, Avci O, Abdeljaber O et al (2019) 1D convolutional neural networks and applications: a survey. arXiv:1905.03554","DOI":"10.1109\/ICASSP.2019.8682194"},{"key":"5393_CR33","doi-asserted-by":"crossref","unstructured":"Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). Association for Computational Linguistics, Stroudsburg, pp 1746\u20131751","DOI":"10.3115\/v1\/D14-1181"},{"key":"5393_CR34","doi-asserted-by":"crossref","unstructured":"Abdel-Hamid O, Mohamed A, Jiang H, Penn G (2012) Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition. In: 2012 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 4277\u20134280","DOI":"10.1109\/ICASSP.2012.6288864"},{"key":"5393_CR35","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1109\/TBME.2015.2468589","volume":"63","author":"S Kiranyaz","year":"2016","unstructured":"Kiranyaz S, Ince T, Gabbouj M (2016) Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans Biomed Eng 63:664\u2013675. https:\/\/doi.org\/10.1109\/TBME.2015.2468589","journal-title":"IEEE Trans Biomed Eng"},{"key":"5393_CR36","doi-asserted-by":"publisher","first-page":"1308","DOI":"10.1016\/j.neucom.2017.09.069","volume":"275","author":"O Abdeljaber","year":"2018","unstructured":"Abdeljaber O, Avci O, Kiranyaz MS et al (2018) 1-D CNNs for structural damage detection: verification on a structural health monitoring benchmark data. Neurocomputing 275:1308\u20131317. https:\/\/doi.org\/10.1016\/j.neucom.2017.09.069","journal-title":"Neurocomputing"},{"key":"5393_CR37","unstructured":"Nair V, Hinton GE (2010) Rectified linear units improve Restricted Boltzmann machines. In: ICML 2010-Proceedings, 27th international conference on machine learning"},{"key":"5393_CR38","unstructured":"Merrill N, Maillart T, Johnson B, Chuang J (2015) Improving physiological signal classification using logarithmic quantization and a progressive calibration technique. In: Proceedings of the 2nd international conference on physiological computing systems. SCITEPRESS-Science and Technology Publications, pp 44\u201351"},{"key":"5393_CR39","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1109\/TPAMI.2008.79","volume":"31","author":"J Wright","year":"2009","unstructured":"Wright J, Yang AY, Ganesh A et al (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31:210\u2013227. https:\/\/doi.org\/10.1109\/TPAMI.2008.79","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5393_CR40","doi-asserted-by":"publisher","first-page":"1877","DOI":"10.1109\/TCYB.2015.2457234","volume":"46","author":"J Gui","year":"2016","unstructured":"Gui J, Liu T, Tao D et al (2016) Representative vector machines: a unified framework for classical classifiers. IEEE Trans Cybern 46:1877\u20131888. https:\/\/doi.org\/10.1109\/TCYB.2015.2457234","journal-title":"IEEE Trans Cybern"},{"key":"5393_CR41","doi-asserted-by":"crossref","unstructured":"Merrill N, Curran MT, Yang J-K, Chuang J (2016) Classifying mental gestures with in-ear EEG. In: 2016 IEEE 13th international conference on wearable and implantable body sensor networks (BSN). IEEE, pp 130\u2013135","DOI":"10.1109\/BSN.2016.7516246"},{"key":"5393_CR42","unstructured":"Ling CX, Huang J, Zhang H (2003) AUC: a statistically consistent and more discriminating measure than accuracy. In: IJCAI international joint conference on artificial intelligence"},{"key":"5393_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/S1076-6332(98)80208-0","author":"NA Obuchowski","year":"1998","unstructured":"Obuchowski NA, Lieber ML (1998) Confidence intervals for the receiver operating characteristic area in studies with small samples. Acad Radiol. https:\/\/doi.org\/10.1016\/S1076-6332(98)80208-0","journal-title":"Acad Radiol"},{"key":"5393_CR44","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A et al (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"5393_CR45","doi-asserted-by":"crossref","unstructured":"Bernard JB, Nicholas MG (2013) The brain is conscious. In: Fundamentals of cognitive neuroscience. Elsevier, pp 211\u2013252","DOI":"10.1016\/B978-0-12-415805-4.00008-4"},{"key":"5393_CR46","doi-asserted-by":"crossref","unstructured":"Garcia-Rill E (2015) The 10\u00a0Hz fulcrum. In: Waking and the reticular activating system in health and disease. Elsevier, pp 157\u2013170. https:\/\/www.sciencedirect.com\/book\/9780128013854\/waking-and-the-reticular-activating-system-in-health-and-disease","DOI":"10.1016\/B978-0-12-801385-4.00008-2"},{"key":"5393_CR47","doi-asserted-by":"crossref","unstructured":"Saidi P, Atia GK, Paris A, Vosoughi A (2015) Motor imagery classification using multiresolution analysis and sparse representation of EEG signals. In: 2015 IEEE global conference on signal and information processing (GlobalSIP). IEEE, pp 815\u2013819","DOI":"10.1109\/GlobalSIP.2015.7418310"},{"key":"5393_CR48","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1016\/j.tics.2012.10.007","volume":"16","author":"W Klimesch","year":"2012","unstructured":"Klimesch W (2012) Alpha-band oscillations, attention, and controlled access to stored information. Trends Cogn Sci 16:606\u2013617. https:\/\/doi.org\/10.1016\/j.tics.2012.10.007","journal-title":"Trends Cogn Sci"},{"key":"5393_CR49","doi-asserted-by":"crossref","unstructured":"Kropotov JD (2009) Alpha rhythms. In: Quantitative EEG, event-related potentials and neurotherapy. Elsevier, pp 29\u201358. https:\/\/www.sciencedirect.com\/book\/9780123745125\/quantitative-eeg-event-related-potentials-and-neurotherapy","DOI":"10.1016\/B978-0-12-374512-5.00002-5"},{"key":"5393_CR50","unstructured":"Nishifuji S, Sato M, Maino D, Tanaka S (2010) Effect of acoustic stimuli and mental task on alpha, beta and gamma rhythms in brain wave. In: Proceedings of the SICE annual conference"},{"key":"5393_CR51","doi-asserted-by":"crossref","unstructured":"Abhang PA, Gawali BW, Mehrotra SC (2016) Technical aspects of brain rhythms and speech parameters. In: Introduction to EEG- and speech-based emotion recognition. Elsevier, pp 51\u201379. https:\/\/www.sciencedirect.com\/book\/9780128044902\/introduction-to-eeg-and-speech-based-emotion-recognition","DOI":"10.1016\/B978-0-12-804490-2.00003-8"},{"key":"5393_CR52","doi-asserted-by":"crossref","unstructured":"Satapathy SK, Dehuri S, Jagadev AK, Mishra S (2019) Introduction. In: EEG brain signal classification for epileptic seizure disorder detection. Elsevier, pp 1\u201325. https:\/\/www.elsevier.com\/books\/eeg-brain-signal-classification-for-epileptic-seizure-disorder-detection\/satapathy\/978-0-12-817426-5","DOI":"10.1016\/B978-0-12-817426-5.00001-6"},{"key":"5393_CR53","doi-asserted-by":"publisher","first-page":"1669","DOI":"10.3390\/s19071669","volume":"19","author":"S Lim","year":"2019","unstructured":"Lim S, Yeo M, Yoon G (2019) Comparison between concentration and immersion based on EEG analysis. Sensors 19:1669. https:\/\/doi.org\/10.3390\/s19071669","journal-title":"Sensors"},{"key":"5393_CR54","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.brainres.2007.07.014","volume":"1169","author":"N Erbil","year":"2007","unstructured":"Erbil N, Ungan P (2007) Changes in the alpha and beta amplitudes of the central EEG during the onset, continuation, and offset of long-duration repetitive hand movements. Brain Res 1169:44\u201356. https:\/\/doi.org\/10.1016\/j.brainres.2007.07.014","journal-title":"Brain Res"},{"key":"5393_CR55","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1053\/apmr.2002.34624","volume":"83","author":"A Craig","year":"2002","unstructured":"Craig A, Moses P, Tran Y et al (2002) The effectiveness of a hands-free environmental control system for the profoundly disabled. Arch Phys Med Rehabil 83:1455\u20131458. https:\/\/doi.org\/10.1053\/apmr.2002.34624","journal-title":"Arch Phys Med Rehabil"},{"key":"5393_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/3789386","volume":"2017","author":"L-W Ko","year":"2017","unstructured":"Ko L-W, Ranga SSK, Komarov O, Chen C-C (2017) Development of single-channel hybrid BCI system using motor imagery and SSVEP. J Healthc Eng 2017:1\u20137. https:\/\/doi.org\/10.1155\/2017\/3789386","journal-title":"J Healthc Eng"},{"key":"5393_CR57","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.bspc.2018.03.010","volume":"44","author":"JM Antelis","year":"2018","unstructured":"Antelis JM, Gudi\u00f1o-Mendoza B, Falc\u00f3n LE et al (2018) Dendrite morphological neural networks for motor task recognition from electroencephalographic signals. Biomed Signal Process Control 44:12\u201324. https:\/\/doi.org\/10.1016\/j.bspc.2018.03.010","journal-title":"Biomed Signal Process Control"},{"key":"5393_CR58","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.neunet.2019.09.037","volume":"122","author":"GCD Virgilio","year":"2020","unstructured":"Virgilio GCD, Sossa AJH, Antelis JM, Falc\u00f3n LE (2020) Spiking neural networks applied to the classification of motor tasks in EEG signals. Neural Netw 122:130\u2013143. https:\/\/doi.org\/10.1016\/j.neunet.2019.09.037","journal-title":"Neural Netw"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05393-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05393-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05393-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T01:03:03Z","timestamp":1633914183000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05393-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,10]]},"references-count":58,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["5393"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05393-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,10]]},"assertion":[{"value":"21 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 September 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2020","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"}},{"value":"Informed consent was obtained from all individual participated in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"All procedures performed in this study were in accordance with the ethical standards of the institutional and national research committee and in a compliance with the 1964 Helsinki Declaration or its later amendments.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal rights"}}]}}