{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T05:16:37Z","timestamp":1770700597946,"version":"3.49.0"},"reference-count":50,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T00:00:00Z","timestamp":1592956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Assessment of brain dynamics elicited by motor imagery (MI) tasks contributes to clinical and learning applications. In this regard, Event-Related Desynchronization\/Synchronization (ERD\/S) is computed from Electroencephalographic signals, which show considerable variations in complexity. We present an Entropy-based method, termed VQEnt, for estimation of ERD\/S using quantized stochastic patterns as a symbolic space, aiming to improve their discriminability and physiological interpretability. The proposed method builds the probabilistic priors by assessing the Gaussian similarity between the input measured data and their reduced vector-quantized representation. The validating results of a bi-class imagine task database (left and right hand) prove that VQEnt holds symbols that encode several neighboring samples, providing similar or even better accuracy than the other baseline sample-based algorithms of Entropy estimation. Besides, the performed ERD\/S time-series are close enough to the trajectories extracted by the variational percentage of EEG signal power and fulfill the physiological MI paradigm. In BCI literate individuals, the VQEnt estimator presents the most accurate outcomes at a lower amount of electrodes placed in the sensorimotor cortex so that reduced channel set directly involved with the MI paradigm is enough to discriminate between tasks, providing an accuracy similar to the performed by the whole electrode set.<\/jats:p>","DOI":"10.3390\/e22060703","type":"journal-article","created":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T08:54:50Z","timestamp":1592988890000},"page":"703","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Entropy-Based Estimation of Event-Related De\/Synchronization in Motor Imagery Using Vector-Quantized Patterns"],"prefix":"10.3390","volume":"22","author":[{"given":"Luisa","family":"Velasquez-Martinez","sequence":"first","affiliation":[{"name":"Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170004, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9561-3800","authenticated-orcid":false,"given":"Juli\u00e1n","family":"Caicedo-Acosta","sequence":"additional","affiliation":[{"name":"Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170004, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0138-5489","authenticated-orcid":false,"given":"Germ\u00e1n","family":"Castellanos-Dominguez","sequence":"additional","affiliation":[{"name":"Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170004, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1038\/s41393-019-0390-1","article-title":"Motor imagery for pain and motor function after spinal cord injury: A systematic review","volume":"58","author":"Opsommer","year":"2019","journal-title":"Spinal Cord"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1080\/10749357.2019.1627716","article-title":"Efficacy of motor imagery additional to motor-based therapy in the recovery of motor function of the upper limb in post-stroke individuals: A systematic review","volume":"26","author":"Machado","year":"2019","journal-title":"Top. Stroke Rehabil."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"396","DOI":"10.3389\/fphys.2019.00396","article-title":"Benefits of Motor Imagery for Human Space Flight: A Brief Review of Current Knowledge and Future Applications","volume":"10","author":"Guillot","year":"2019","journal-title":"Front. Physiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.neubiorev.2019.02.002","article-title":"Motor imagery in children with DCD: A systematic and meta-analytic review of hand-rotation task performance","volume":"99","author":"Barhoun","year":"2019","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.jphys.2019.08.007","article-title":"Motor imagery training improves balance and mobility outcomes in older adults: A systematic review","volume":"65","author":"Nicholson","year":"2019","journal-title":"J. Physiother."},{"key":"ref_6","unstructured":"Frau-Meigs, D. (2007). Media Education. A Kit for Teachers, Students, Parents and Professionals, UNESCO."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Balamurugan, B., Mullai, M., Soundararajan, S., Selvakanmani, S., and Arun, D. (2020). Brain\u2013computer interface for assessment of mental efforts in e-learning using the nonmarkovian queueing model. Comput. Appl. Eng. Educ.","DOI":"10.1002\/cae.22209"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/BF02345286","article-title":"Time-frequency microstructure of event-related electro-encephalogram desynchronisation and synchronisation","volume":"39","author":"Durka","year":"2001","journal-title":"Med Biol. Eng. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"236","DOI":"10.3389\/fpsyg.2011.00236","article-title":"Single-Trial Normalization for Event-Related Spectral Decomposition Reduces Sensitivity to Noisy Trials","volume":"2","author":"Grandchamp","year":"2011","journal-title":"Front. Psychol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1109\/TBME.2014.2312397","article-title":"Brain\u2013computer interfaces using sensorimotor rhythms: Current state and future perspectives","volume":"61","author":"Yuan","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"113285","DOI":"10.1016\/j.eswa.2020.113285","article-title":"Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network","volume":"149","author":"Tang","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"036023","DOI":"10.1088\/1741-2560\/10\/3\/036023","article-title":"Event-related desynchronization and synchronization quantification in motor-related EEG by Kolmogorov entropy","volume":"10","author":"Gao","year":"2013","journal-title":"J. Neural Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104833","DOI":"10.1109\/ACCESS.2019.2930625","article-title":"Fuzzy Entropy Metrics for the Analysis of Biomedical Signals: Assessment and Comparison","volume":"7","author":"Azami","year":"2019","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1550037","DOI":"10.1142\/S0129065715500379","article-title":"Assembling A Multi-Feature EEG Classifier for Left-Right Motor Imagery Data Using Wavelet-Based Fuzzy Approximate Entropy for Improved Accuracy","volume":"25","year":"2015","journal-title":"Int. J. Neural Syst."},{"key":"ref_15","first-page":"857","article-title":"An entropy fusion method for feature extraction of EEG","volume":"29","author":"Shunfei","year":"2016","journal-title":"Neural Comput. Appl."},{"key":"ref_16","first-page":"18","article-title":"Brain Computer Interface issues on hand movement","volume":"30","author":"Pattnaik","year":"2018","journal-title":"Comput. Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1109\/TNSRE.2019.2922713","article-title":"Frequency-Optimized Local Region Common Spatial Pattern Approach for Motor Imagery Classification","volume":"27","author":"Park","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1109\/TNSRE.2017.2778178","article-title":"Evidence of Variabilities in EEG Dynamics During Motor Imagery-Based Multiclass Brain-Computer Interface","volume":"26","author":"Saha","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_19","unstructured":"Jain, S., Sood, M., and Paul, S. (2020). Development of an Effective Computing Framework for Classification of Motor Imagery EEG Signals for Brain\u2013Computer Interface. Advances in Computational Intelligence Techniques, Springer."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/LSP.2016.2542881","article-title":"Dispersion Entropy: A Measure for Time-Series Analysis","volume":"23","author":"Rostaghi","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.bandc.2018.03.010","article-title":"A practical comparison of algorithms for the measurement of multiscale entropy in neural time series data","volume":"123","author":"Kuntzelman","year":"2018","journal-title":"Brain Cogn."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, Y., Gao, X., and Wang, L. (2019). Reverse Dispersion Entropy: A New Complexity Measure for Sensor Signal. Sensors, 19.","DOI":"10.3390\/s19235203"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kafantaris, E., Piper, I., Lo, M., and Escudero, J. (2020). Augmentation of Dispersion Entropy for Handling Missing and Outlier Samples in Physiological Signal Monitoring. Entropy, 22.","DOI":"10.3390\/e22030319"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"023111","DOI":"10.1063\/1.5136246","article-title":"Motor execution reduces EEG signals complexity: Recurrence quantification analysis study<? A3B2 show [feature]?>","volume":"30","author":"Pitsik","year":"2020","journal-title":"Chaos Interdiscip. J. Nonlinear Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.jneumeth.2016.12.010","article-title":"Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: An sparse regression and Weighted Na\u00efve Bayesian Classifier-based approach","volume":"278","author":"Miao","year":"2017","journal-title":"J. Neurosci. Methods"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"016002","DOI":"10.1088\/1741-2560\/10\/1\/016002","article-title":"Adaptive Laplacian filtering for sensorimotor rhythm-based brain\u2013computer interfaces","volume":"10","author":"Lu","year":"2012","journal-title":"J. Neural Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sannelli, C., Vidaurre, C., M\u00fcller, K., and Blankertz, B. (September, January 31). Common spatial pattern patches-an optimized filter ensemble for adaptive brain-computer interfaces. Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina.","DOI":"10.1109\/IEMBS.2010.5626227"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Delgado-Bonal, A., and Marshak, A. (2019). Approximate Entropy and Sample Entropy: A Comprehensive Tutorial. Entropy, 21.","DOI":"10.3390\/e21060541"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Nguyen, T., and Nguyen, T. (2020). Entropy-Constrained Maximizing Mutual Information Quantization. arXiv.","DOI":"10.1109\/TCOMM.2020.3002910"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2759","DOI":"10.1016\/j.sigpro.2013.02.012","article-title":"Fixed budget quantized kernel least-mean-square algorithm","volume":"93","author":"Zhao","year":"2013","journal-title":"Signal Process."},{"key":"ref_31","first-page":"1","article-title":"Adaptive Bayesian label fusion using kernel-based similarity metrics in hippocampus segmentation","volume":"6","year":"2019","journal-title":"J. Med. Imaging"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3322","DOI":"10.1109\/TCYB.2018.2841847","article-title":"Temporally constrained sparse group spatial patterns for motor imagery BCI","volume":"49","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"ref_33","unstructured":"Latchoumane, C., Chung, D., Kim, S., and Jeong, J. (2007, January 25\u201327). Segmentation and Characterization of EEG During Mental tasks using Dynamical Nonstationarity. Proceedings of the Computational Intelligence in Medical and Healthcare (CIMED 2007), Plymouth, UK."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ma, M., Guo, L., Su, K., and Liang, D. (2017, January 25\u201326). Classification of motor imagery EEG signals based on wavelet transform and sample entropy. Proceedings of the 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China.","DOI":"10.1109\/IAEAC.2017.8054145"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ahn, M., Cho, H.n., Ahn, S., and Jun, S. (2013). High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0080886"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"155","DOI":"10.3389\/fnins.2020.00155","article-title":"Enhanced Multiple Instance Representation Using Time-Frequency Atoms in Motor Imagery Classification","volume":"14","year":"2020","journal-title":"Front. Neurosci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/S0013-4694(97)88021-6","article-title":"EEG event-related desynchronization (ERD) and synchronization (ERS)","volume":"1","author":"Pfurtscheller","year":"1997","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.jneumeth.2015.01.033","article-title":"Performance variation in motor imagery brain\u2013computer interface: A brief review","volume":"243","author":"Ahn","year":"2015","journal-title":"J. Neurosci. Methods"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Velasquez-Martinez, L., Arteaga, F., and Castellanos-Dominguez, G. (2019, January 13\u201315). Subject-Oriented Dynamic Characterization of Motor Imagery Tasks Using Complexity Analysis. Proceedings of the International Conference on Brain Informatics, Haikou, China.","DOI":"10.1007\/978-3-030-37078-7_3"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.jneumeth.2007.03.024","article-title":"Nonparametric statistical testing of EEG-and MEG-data","volume":"164","author":"Maris","year":"2007","journal-title":"J. Neurosci. Methods"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.neuroimage.2015.04.020","article-title":"Real-time EEG feedback during simultaneous EEG\u2013fMRI identifies the cortical signature of motor imagery","volume":"114","author":"Zich","year":"2015","journal-title":"Neuroimage"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"848","DOI":"10.3389\/fnhum.2013.00848","article-title":"Gamma band activity associated with BCI performance: Simultaneous MEG\/EEG study","volume":"7","author":"Ahn","year":"2013","journal-title":"Front. Hum. Neurosci."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Giusti, R., and Batista, G.E. (2013, January 19\u201324). An empirical comparison of dissimilarity measures for time series classification. Proceedings of the 2013 Brazilian Conference on Intelligent Systems, Fortaleza, Brazil.","DOI":"10.1109\/BRACIS.2013.22"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Xygonakis, I., Athanasiou, A., Pandria, N., Kugiumtzis, D., and Bamidis, P.D. (2018). Decoding motor imagery through common spatial pattern filters at the EEG source space. Comput. Intell. Neurosci., 2018.","DOI":"10.1155\/2018\/7957408"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1080\/08990220.2019.1699044","article-title":"Cerebral haemodynamics during motor imagery of self-feeding with chopsticks: Differences between dominant and non-dominant hand","volume":"37","author":"Matsuo","year":"2020","journal-title":"Somatosens. Mot. Res."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Allison, B.Z., and Neuper, C. (2010). Could anyone use a BCI?. Brain-Computer Interfaces, Springer.","DOI":"10.1007\/978-1-84996-272-8_3"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-24535-4","article-title":"Cortical classification with rhythm entropy for error processing in cocktail party environment based on scalp EEG recording","volume":"8","author":"Tian","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/MSP.2008.4408441","article-title":"Optimizing spatial filters for robust EEG single-trial analysis","volume":"25","author":"Blankertz","year":"2007","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3069","DOI":"10.1016\/j.physd.2008.06.005","article-title":"The effect of time delay on Approximate and Sample Entropy calculations","volume":"237","author":"Kaffashi","year":"2008","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1007\/s11948-018-0061-1","article-title":"Critiquing the concept of BCI illiteracy","volume":"25","author":"Thompson","year":"2019","journal-title":"Sci. Eng. 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