{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T20:12:59Z","timestamp":1784578379627,"version":"3.55.0"},"reference-count":51,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2019,10,18]],"date-time":"2019-10-18T00:00:00Z","timestamp":1571356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Brain-Computer Interfaces (BCI) are systems that allow the interaction of people and devices on the grounds of brain activity. The noninvasive and most viable way to obtain such information is by using electroencephalography (EEG). However, these signals have a low signal-to-noise ratio, as well as a low spatial resolution. This work proposes a new method built from the combination of a Blind Source Separation (BSS) to obtain estimated independent components, a 2D representation of these component signals using the Continuous Wavelet Transform (CWT), and a classification stage using a Convolutional Neural Network (CNN) approach. A criterion based on the spectral correlation with a Movement Related Independent Component (MRIC) is used to sort the estimated sources by BSS, thus reducing the spatial variance. The experimental results of 94.66% using a k-fold cross validation are competitive with techniques recently reported in the state-of-the-art.<\/jats:p>","DOI":"10.3390\/s19204541","type":"journal-article","created":{"date-parts":[[2019,10,18]],"date-time":"2019-10-18T11:24:15Z","timestamp":1571397855000},"page":"4541","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["A New Approach for Motor Imagery Classification Based on Sorted Blind Source Separation, Continuous Wavelet Transform, and Convolutional Neural Network"],"prefix":"10.3390","volume":"19","author":[{"given":"C\u00e9sar J.","family":"Ortiz-Echeverri","sequence":"first","affiliation":[{"name":"Facultad de Inform\u00e1tica, Universidad Aut\u00f3noma de Quer\u00e9taro, C.P. 76230 Quer\u00e9taro, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6353-0864","authenticated-orcid":false,"given":"Sebasti\u00e1n","family":"Salazar-Colores","sequence":"additional","affiliation":[{"name":"Facultad de Inform\u00e1tica, Universidad Aut\u00f3noma de Quer\u00e9taro, C.P. 76230 Quer\u00e9taro, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8598-5600","authenticated-orcid":false,"given":"Juvenal","family":"Rodr\u00edguez-Res\u00e9ndiz","sequence":"additional","affiliation":[{"name":"Facultad de Ingnier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro, C.P. 76010 Quer\u00e9taro, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberto A.","family":"G\u00f3mez-Loenzo","sequence":"additional","affiliation":[{"name":"Facultad de Ingnier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro, C.P. 76010 Quer\u00e9taro, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.neucom.2014.12.114","article-title":"Evaluating Quantum Neural Network filtered motor imagery brain-computer interface using multiple classification techniques","volume":"170","author":"Gandhi","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sanei, S., and Chambers, J.A. (2007). EEG Signal Processing, Wiley.","DOI":"10.1002\/9780470511923"},{"key":"ref_3","unstructured":"Niedermeyer, E., and da Silva, F.L. (2005). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, Lippincott Williams & Wilkins."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"17849","DOI":"10.1073\/pnas.0403504101","article-title":"Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans","volume":"101","author":"Wolpaw","year":"2004","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.brainresrev.2005.04.005","article-title":"The functional significance of mu rhythms: Translating \u201cseeing\u201d and \u201chearing\u201d into \u201cdoing\u201d","volume":"50","author":"Pineda","year":"2005","journal-title":"Brain Res. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1023\/A:1023437823106","article-title":"Mu and beta rhythm topographies during motor imagery and actual movements","volume":"12","author":"McFarland","year":"2000","journal-title":"Brain Topogr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"046021","DOI":"10.1088\/1741-2552\/aac313","article-title":"Multiband tangent space mapping and feature selection for classification of EEG during motor imagery","volume":"15","author":"Islam","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/0013-4694(79)90063-4","article-title":"Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement","volume":"46","author":"Pfurtscheller","year":"1979","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/0013-4694(81)90139-5","article-title":"Central beta rhythm during sensorimotor activities in man","volume":"51","author":"Pfurtscheller","year":"1981","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/0304-3940(94)90127-9","article-title":"Event-related synchronization of mu rhythm in the EEG over the cortical hand area in man","volume":"174","author":"Pfurtscheller","year":"1994","journal-title":"Neurosci. Lett."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.neuroimage.2005.12.003","article-title":"Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks","volume":"31","author":"Pfurtscheller","year":"2006","journal-title":"NeuroImage"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/0013-4694(95)00083-B","article-title":"Spatio-temporal decomposition of the EEG: A general approach to the isolation and localization of sources","volume":"95","author":"Koles","year":"1995","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, A., Chen, K., Liu, Q., Ai, Q., Xie, Y., and Chen, A. (2017). Feature selection for motor imagery EEG classification based on firefly algorithm and learning automata. Sensors, 17.","DOI":"10.3390\/s17112576"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1088\/1741-2560\/3\/3\/003","article-title":"Seperability of four-class motor imagery data using independent components analysis","volume":"3","author":"Naeem","year":"2006","journal-title":"J. Neural Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.bspc.2011.06.005","article-title":"Blind source separation, wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling","volume":"7","author":"Ranta","year":"2012","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1016\/S1388-2457(00)00386-2","article-title":"Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects","volume":"111","author":"Jung","year":"2000","journal-title":"Clin. Neurophysiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","article-title":"EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis","volume":"134","author":"Delorme","year":"2004","journal-title":"J. Neurosci. Methods"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mur, A., Dormido, R., and Duro, N. (2019). An Unsupervised Method for Artefact Removal in EEG Signals. Sensors, 19.","DOI":"10.3390\/s19102302"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, Y.T., and Jung, T.P. (2012). Translation of EEG spatial filters from resting to motor imagery using independent component analysis. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0037665"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.bspc.2019.01.017","article-title":"How many channels are suitable for independent component analysis in motor imagery brain-computer interface","volume":"50","author":"Zhou","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_22","unstructured":"Chiu, C.Y., Chen, C.Y., Lin, Y.Y., Chen, S.A., and Lin, C.T. (2014, January 12\u201314). Using a novel LDA-ensemble framework to classification of motor imagery tasks for brain-computer interface applications. Proceedings of the Intelligent Systems and Applications: Proceedings of the International Computer Symposium (ICS), Taichung, Taiwan."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/s11760-014-0736-2","article-title":"Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection","volume":"10","author":"Das","year":"2016","journal-title":"Signal Image Video Process."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chatterjee, R., and Bandyopadhyay, T. (2016, January 11). EEG based Motor Imagery Classification using SVM and MLP. Proceedings of the 2nd International Conference on Computational Intelligence and Networks (CINE), Bhubaneswar, India.","DOI":"10.1109\/CINE.2016.22"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1109\/TSMC.2015.2450680","article-title":"Common Bayesian network for classification of EEG-based multiclass motor imagery BCI","volume":"46","author":"He","year":"2015","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., and Wang, Y. (2017). Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction. Sensors, 17.","DOI":"10.3390\/s17040818"},{"key":"ref_27","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_28","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":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5391","DOI":"10.1002\/hbm.23730","article-title":"Deep learning with convolutional neural networks for EEG decoding and visualization","volume":"38","author":"Schirrmeister","year":"2017","journal-title":"Hum. Brain Mapp."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.ijleo.2016.10.117","article-title":"Single-trial EEG classification of motor imagery using deep convolutional neural networks","volume":"130","author":"Tang","year":"2017","journal-title":"Opt. Int. J. Light Electron Opt."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tayeb, Z., Fedjaev, J., Ghaboosi, N., Richter, C., Everding, L., Qu, X., Wu, Y., Cheng, G., and Conradt, J. (2019). Validating deep neural networks for online decoding of motor imagery movements from EEG signals. Sensors, 19.","DOI":"10.3390\/s19010210"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"15945","DOI":"10.1109\/ACCESS.2019.2895133","article-title":"A novel deep learning approach with data augmentation to classify motor imagery signals","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_33","unstructured":"Zhang, X., Yao, L., Wang, X., Monaghan, J., and Mcalpine, D. (2019). A Survey on Deep Learning based Brain Computer Interface: Recent Advances and New Frontiers. arXiv."},{"key":"ref_34","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":"2016","journal-title":"J. Neural Eng."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T.H., and Faubert, J. (2019). Deep learning-based electroencephalography analysis: A systematic review. J. Neural Eng., 16.","DOI":"10.1088\/1741-2552\/ab260c"},{"key":"ref_36","unstructured":"Belouchrani, A., Abed-Meraim, K., Cardoso, J., and Moulines, E. (1993). Second-order blind separation of temporally correlated sources. Proc. Int. Conf. Digit. Signal Process., 346\u2013351."},{"key":"ref_37","unstructured":"Comon, P., and Jutten, C. (2010). Handbook of Blind Source Separation: Independent Component Analysis and Applications, Academic Press."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/S0893-6080(00)00026-5","article-title":"Independent component analysis: Algorithms and applications","volume":"13","author":"Oja","year":"2000","journal-title":"Neural Netw."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Mallat, S. (1999). A Wavelet Tour of Signal Processing, Elsevier.","DOI":"10.1016\/B978-012466606-1\/50008-8"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4790","DOI":"10.1364\/AO.29.004790","article-title":"Parallel distributed processing model with local space-invariant interconnections and its optical architecture","volume":"29","author":"Zhang","year":"1990","journal-title":"Appl. Opt."},{"key":"ref_41","unstructured":"(2019, September 09). A Comprehensive Guide to Convolutional Neural Networks\u2014The ELI5 Way. Available online: https:\/\/towardsdatascience.com\/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53."},{"key":"ref_42","unstructured":"(2019, September 09). Data Set IVa. Available online: http:\/\/www.bbci.de\/competition\/iii\/desc_IVa.html."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1109\/TNSRE.2006.875642","article-title":"The BCI competition III: Validating alternative approaches to actual BCI problems","volume":"14","author":"Blankertz","year":"2006","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_44","unstructured":"(2019, August 10). Morse Wavelets. Available online: https:\/\/la.mathworks.com\/help\/wavelet\/ug\/morse-wavelets.html."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neunet.2017.01.005","article-title":"A new method for quantifying the performance of EEG blind source separation algorithms by referencing a simultaneously recorded ECoG signal","volume":"93","author":"Oosugi","year":"2017","journal-title":"Neural Netw."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s11517-009-0452-1","article-title":"Independent component analysis: Comparison of algorithms for the investigation of surface electrical brain activity","volume":"47","author":"Klemm","year":"2009","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_47","first-page":"407","article-title":"ICA-based EEG denoising: A comparative analysis of fifteen methods","volume":"60","author":"Albera","year":"2012","journal-title":"Bull. Pol. Acad. Sci. Tech. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1109\/TNSRE.2012.2184838","article-title":"Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain\u2013computer interface","volume":"20","author":"Siuly","year":"2012","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.measurement.2016.02.059","article-title":"Detection of motor imagery EEG signals employing Na\u00efve Bayes based learning process","volume":"86","author":"Wang","year":"2016","journal-title":"Measurement"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.bspc.2016.09.007","article-title":"Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system","volume":"31","author":"Kevric","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Taran, S., and Bajaj, V. (2018). Motor imagery tasks-based EEG signals classification using tunable-Q wavelet transform. Neural Comput. Appl., 1\u20138.","DOI":"10.1007\/s00521-018-3531-0"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/20\/4541\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:27:43Z","timestamp":1760189263000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/20\/4541"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,18]]},"references-count":51,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["s19204541"],"URL":"https:\/\/doi.org\/10.3390\/s19204541","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,18]]}}}