{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T11:19:50Z","timestamp":1777115990765,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,12,5]],"date-time":"2019-12-05T00:00:00Z","timestamp":1575504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["2018-0-00735"],"award-info":[{"award-number":["2018-0-00735"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The motor imagery-based brain-computer interface (BCI) using electroencephalography (EEG) has been receiving attention from neural engineering researchers and is being applied to various rehabilitation applications. However, the performance degradation caused by motor imagery EEG with very low single-to-noise ratio faces several application issues with the use of a BCI system. In this paper, we propose a novel motor imagery classification scheme based on the continuous wavelet transform and the convolutional neural network. Continuous wavelet transform with three mother wavelets is used to capture a highly informative EEG image by combining time-frequency and electrode location. A convolutional neural network is then designed to both classify motor imagery tasks and reduce computation complexity. The proposed method was validated using two public BCI datasets, BCI competition IV dataset 2b and BCI competition II dataset III. The proposed methods were found to achieve improved classification performance compared with the existing methods, thus showcasing the feasibility of motor imagery BCI.<\/jats:p>","DOI":"10.3390\/e21121199","type":"journal-article","created":{"date-parts":[[2019,12,5]],"date-time":"2019-12-05T11:16:31Z","timestamp":1575544591000},"page":"1199","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":104,"title":["Application of Continuous Wavelet Transform and Convolutional Neural Network in Decoding Motor Imagery Brain-Computer Interface"],"prefix":"10.3390","volume":"21","author":[{"given":"Hyeon Kyu","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Electronics and Communications Engineering, Kwangwoon University, Seoul 01897, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8292-7093","authenticated-orcid":false,"given":"Young-Seok","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communications Engineering, Kwangwoon University, Seoul 01897, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1109\/TBME.2014.2300164","article-title":"Visual and Auditory Brain\u2013Computer Interfaces","volume":"61","author":"Gao","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1016\/S0013-4694(97)00080-1","article-title":"EEG-based discrimination between imagination of right and left hand movement","volume":"103","author":"Pfurtscheller","year":"1997","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1109\/TCIAIG.2012.2237173","article-title":"Two Brains, One Game: Design and Evaluation of a Multiuser BCI Video Game Based on Motor Imagery","volume":"5","author":"Bonnet","year":"2013","journal-title":"IEEE Trans. Comput. Intell. AI Games"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2516","DOI":"10.1109\/TNSRE.2017.2766365","article-title":"Self-Paced Operation of a Wheelchair Based on a Hybrid Brain-Computer Interface Combining Motor Imagery and P300 Potential","volume":"25","author":"Yu","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1016\/j.neuroimage.2010.03.022","article-title":"Neurophysiological predictor of SMR-based BCI performance","volume":"51","author":"Blankertz","year":"2010","journal-title":"Neuroimage"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"13222","DOI":"10.1038\/s41598-018-31472-9","article-title":"Attending to Visual Stimuli versus Performing Visual Imagery as a Control Strategy for EEG-based Brain-Computer Interfaces","volume":"8","author":"Kosmyna","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1109\/TRE.2000.847807","article-title":"Brain-computer interface technology: A review of the first international meeting","volume":"8","author":"Wolpaw","year":"2000","journal-title":"IEEE Trans. Rehabil. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.3390\/s120201211","article-title":"Brain Computer Interfaces, a Review","volume":"12","year":"2012","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dai, M., Zheng, D., Na, R., Wang, S., and Zhang, S. (2019). EEG Classification of Motor Imagery Using a Novel Deep Learning Framework. Sensors, 19.","DOI":"10.3390\/s19030551"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1109\/86.895946","article-title":"Optimal spatial filtering of single trial EEG during imagined hand movement","volume":"8","author":"Ramoser","year":"2000","journal-title":"IEEE Trans. Rehabil. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mart\u00edn-Clemente, R., Olias, J., Thiyam, D.B., Cichocki, A., and Cruces, S. (2018). Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison. Entropy, 20.","DOI":"10.3390\/e20010007"},{"key":"ref_12","unstructured":"Ang, K.K., Chin, Z.Y., Zhang, H., and Guan, C. (2008, January 1\u20138). Filter Bank Common Spatial Pattern (FBCSP). Proceedings of the International Joint Conference on Neural Networks (IJCNN), Hong Kong, China."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/TNSRE.2017.2757519","article-title":"Filter Bank Regularized Common Spatial Pattern Ensemble for Small Sample Motor Imagery Classification","volume":"26","author":"Park","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jolliffe, I. (2011). Principal component analysis. International Encyclopedia of Statistical Science, Springer.","DOI":"10.1007\/978-3-642-04898-2_455"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/0165-1684(94)90029-9","article-title":"Independent component analysis, A new concept?","volume":"36","author":"Comon","year":"1994","journal-title":"Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.jneumeth.2008.09.014","article-title":"EEG-based motor imagery analysis using weighted wavelet transform features","volume":"176","author":"Hsu","year":"2009","journal-title":"J. Neurosci. Methods"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"031005","DOI":"10.1088\/1741-2552\/aab2f2","article-title":"A review of classification algorithms for EEG-based brain\u2013computer interfaces: A 10 year update","volume":"15","author":"Lotte","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1109\/LSP.2009.2022557","article-title":"Composite common spatial pattern for subject-to-subject transfer","volume":"16","author":"Kang","year":"2009","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1016\/j.neunet.2009.06.003","article-title":"Subject-independent mental state classification in single trials","volume":"22","author":"Fazli","year":"2009","journal-title":"Neural Netw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"066009","DOI":"10.1088\/1741-2560\/12\/6\/066009","article-title":"Increasing session-to-session transfer in a brain\u2013computer interface with on-site background noise acquisition","volume":"12","author":"Cho","year":"2015","journal-title":"J. Neural Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/TNSRE.2016.2601240","article-title":"A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines","volume":"25","author":"Lu","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Simard, P., Steinkraus, D., and Platt, J.C. (2003). Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis. Seventh International Conference on Document Analysis and Recognition, IEEE.","DOI":"10.1109\/ICDAR.2003.1227801"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bengio, Y., and LeCun, Y. (2007). Scaling Learning Algorithms towards AI. Large-Scale Kernel Machines, MIT Press.","DOI":"10.7551\/mitpress\/7496.003.0016"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"016003","DOI":"10.1088\/1741-2560\/14\/1\/016003","article-title":"A novel deep learning approach for classification of EEG motor imagery signals","volume":"14","author":"Tabar","year":"2017","journal-title":"J. Neural Eng."},{"key":"ref_26","unstructured":"(2019, January 25). BCI Competitions. Available online: http:\/\/www.bbci.de\/competition\/."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1109\/TNSRE.2007.906956","article-title":"Brain\u2013Computer Communication: Motivation, Aim, and Impact of Exploring a Virtual Apartment","volume":"15","author":"Leeb","year":"2007","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_28","unstructured":"Leeb, R., Brunner, C., Mueller-Put, G., Schloegl, A., and Pfurtscheller, G. (2008). BCI Competition 2008-Graz Data Set b, Graz University of Technology."},{"key":"ref_29","first-page":"1457","article-title":"Classification of motor imagery movements using multivariate empirical mode decomposition and short time Fourier transform based hybrid method","volume":"19","author":"Bashar","year":"2016","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"G\u00f3mez, M.J., Castej\u00f3n, C., and Garc\u00eda-Prada, J.C. (2016). Review of Recent Advances in the Application of the Wavelet Transform to Diagnose Cracked Rotors. Algorithms, 9.","DOI":"10.3390\/a9010019"},{"key":"ref_31","unstructured":"Auger, F., Patrick, F., Paulo, G., and Olivier, L. (1996). Time-Frequency Toolbox, CNRS France-Rice University."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5787","DOI":"10.1109\/TSP.2012.2212891","article-title":"A New Algorithm for Multicomponent Signals Analysis Based on SynchroSqueezing: With an Application to Signal Sampling and Denoising","volume":"60","author":"Meignen","year":"2012","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Landau, R.H., Paez, J., and Bordeianu, C.C. (2008). A Survey of Computational Physics: Introductory Computational Science, Princeton University Press.","DOI":"10.1515\/9781400841189"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1016\/S1388-2457(99)00141-8","article-title":"Event-related EEG\/MEG synchronization and desynchronization: Basic principles","volume":"110","author":"Pfurtscheller","year":"1999","journal-title":"Clin. Neurophysiol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tang, Z., Sun, S., Zhang, S., Chen, Y., Li, C., and Chen, S. (2016). A Brain-Machine Interface Based on ERD\/ERS for an Upper-Limb Exoskeleton Control. Sensors, 16.","DOI":"10.3390\/s16122050"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.ergon.2011.03.005","article-title":"Event-related (De)synchronization (ERD\/ERS) during motor imagery tasks: Implications for brain\u2013computer interfaces","volume":"41","author":"Jeon","year":"2011","journal-title":"Int. J. Ind. Ergon."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/12\/1199\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:40:43Z","timestamp":1760190043000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/12\/1199"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,5]]},"references-count":36,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["e21121199"],"URL":"https:\/\/doi.org\/10.3390\/e21121199","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,5]]}}}