Discriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraints

graficas, tablas

Autores:: Velásquez Martínez, Luisa Fernanda

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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network_acronym_str	UNACIONAL2
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repository_id_str
dc.title.eng.fl_str_mv	Discriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraints
dc.title.translated.spa.fl_str_mv	Discriminación de estados mentales mediante la extracción de patrones espaciales bajo restricciones de no estacionariedad e independencia de sujeto
title	Discriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraints
spellingShingle	Discriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraints 000 - Ciencias de la computación, información y obras generales Motor Imagery Event-related synchronization Entropy Brain-computer interface BCI inefficiency Regression networks Multi-subject analysis Imaginación Motora Sincronización relacionada a eventos Entropia Interfaces cerebro computador Ineficiencia en BCI Redes de regresión Análisis multi-sujeto
title_short	Discriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraints
title_full	Discriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraints
title_fullStr	Discriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraints
title_full_unstemmed	Discriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraints
title_sort	Discriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraints
dc.creator.fl_str_mv	Velásquez Martínez, Luisa Fernanda
dc.contributor.advisor.none.fl_str_mv	Castellanos Dominguez, Cesar Germán
dc.contributor.author.none.fl_str_mv	Velásquez Martínez, Luisa Fernanda
dc.contributor.researchgroup.spa.fl_str_mv	Grupo de Control y Procesamiento Digital de Señales
dc.contributor.googlescholar.spa.fl_str_mv	https://scholar.google.com.co/citations?user=kzOD4RQAAAAJ&hl=en
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales
topic	000 - Ciencias de la computación, información y obras generales Motor Imagery Event-related synchronization Entropy Brain-computer interface BCI inefficiency Regression networks Multi-subject analysis Imaginación Motora Sincronización relacionada a eventos Entropia Interfaces cerebro computador Ineficiencia en BCI Redes de regresión Análisis multi-sujeto
dc.subject.proposal.eng.fl_str_mv	Motor Imagery Event-related synchronization Entropy Brain-computer interface BCI inefficiency Regression networks Multi-subject analysis
dc.subject.proposal.spa.fl_str_mv	Imaginación Motora Sincronización relacionada a eventos Entropia Interfaces cerebro computador Ineficiencia en BCI Redes de regresión Análisis multi-sujeto
description	graficas, tablas
publishDate	2023
dc.date.accessioned.none.fl_str_mv	2023-08-14T20:31:52Z
dc.date.available.none.fl_str_mv	2023-08-14T20:31:52Z
dc.date.issued.none.fl_str_mv	2023
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv	Image Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TD
format	http://purl.org/coar/resource_type/c_db06
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/84556
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/84556 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.references.spa.fl_str_mv	B. Babadi, "Neural encoding and decoding," Handbook of Neuroengineering, pp. 1-24, 2020. A. Singh, A. A. Hussain, S. Lal, and H. W. Guesgen, \A comprehensive review on critical issues and possible solutions of motor imagery based electroencephalography brain-computer interface," Sensors, vol. 21, no. 6, p. 2173, 2021. A. M. Alvarez-Meza, L. F. Velasquez-Martinez, and G. Castellanos-Dominguez, "Time-series discrimination using feature relevance analysis in motor imagery classi cation," Neurocomputing, vol. 151, pp. 122-129, 2015. Y. K. Stolbkov, T. R. Moshonkina, I. V. Orlov, E. S. Tomilovskaya, I. B. Kozlovskaya, and Y. P. Gerasimenko, "The neurophysiological correlates of real and imaginary locomotion," Human Physiology, vol. 45, no. 1, pp. 104-114, 2019. B. Fadlallah, S. Seth, A. Keil, and J. Principe, "Quantifying cognitive state from eeg using dependence measures," IEEE Transactions on Biomedical Engineering, vol. 59, no. 10, pp. 2773-2781, 2012. Z. Chen, "A primer on neural signal processing," IEEE Circuits and Systems Magazine, vol. 17, no. 1, pp. 33-50, 2017 S. N. Abdulkader, A. Atia, and M.-S. M. Mostafa, "Brain-computer interfacing: Applications and challenges," Egyptian Informatics Journal, vol. 16, no. 2, pp. 213-230, 2015. M. W. Woolrich, A. Baker, H. Luckhoo, H. Mohseni, G. Barnes, M. Brookes, and I. Rezek, "Dynamic state allocation for meg source reconstruction," Neuroimage, vol. 77, pp. 77-92, 2013. C. Guerrero-Mosquera and A. Navia-Vazquez, "Automatic removal of ocular artefacts using adaptive filtering and independent component analysis for electroencephalogram data," IET signal processing, vol. 6, no. 2, pp. 99-106, 2012. M. Saeidi, W. Karwowski, F. V. Farahani, K. Fiok, R. Taiar, P. Hancock, and A. AlJuaid, "Neural decoding of eeg signals with machine learning: a systematic review," Brain Sciences, vol. 11, no. 11, p. 1525, 2021. D. R. Ripoll, "Introducción a la organización del sistema nervioso," in Neurociencia cognitiva. Editorial Médica Panamericana, 2013, pp. 67-110. Y.-H. Liu, L.-F. Lin, C.-W. Chou, Y. Chang, Y.-T. Hsiao, and W.-C. Hsu, "Analysis of electroencephalography event-related desynchronisation and synchronisation induced by lower-limb stepping motor imagery," Journal of Medical and Biological Engineering, vol. 39, no. 1, pp. 54-69, 2019. G. Rodríguez-Bermúdez and P. J. Garcíaa-Laencina, "Automatic and adaptive classi cation of electroencephalographic signals for brain-computer interfaces," Journal of medical systems, vol. 36, no. 1, pp. 51-63, 2012. S. Lemm, B. Blankertz, T. Dickhaus, and K.-R. M uller, "Introduction to machine learning for brain imaging," Neuroimage, vol. 56, no. 2, pp. 387-399, 2011. E. Opsommer, O. Chevalley, and N. Korogod, "Motor imagery for pain and motor function after spinal cord injury: a systematic review," Spinal Cord, vol. 58, pp. 262-274, 2019. T. Machado, A. Carregosa, M. Santos, N. da Silva, and M. Melo, "E cacy 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," Topics in Stroke Rehabilitation, vol. 26, no. 7, pp. 548-553, 2019. G. Aymeric and D. Ursula, \Bene ts of motor imagery for human space flight: A brief review of current knowledge and future applications," Frontiers in Physiology, no. 10, p. 396, 2019. P. Barhoun, I. Fuelscher, E. Kothe, J. He, G. Youssef, P. Enticott, J. Williams, and C. Hyde, "Motor imagery in children with dcd: A systematic and meta-analytic review of hand-rotation task performance," Neuroscience & Biobehavioral Reviews, vol. 99, pp. 282-297, 2019. V. Nicholson, N. Watts, Y. Chani, and J. Keogh, "Motor imagery training improves balance and mobility outcomes in older adults: a systematic review," Journal of Physiotherapy, vol. 65, no. 4, pp. 200 - 207, 2019. T. MacIntyre, M. A. Madan, C, C. Collet, and A. Guillot, "Motor imagery, performance and motor rehabilitation," vol. 240, pp. 141-159, 2018. S. G. Boe and S. N. Kraeutner, "Assessing motor imagery ability through imagery-based learning: An overview and introduction to miscreen, a mobile app for imagery assessment," Imagination, Cognition and Personality, vol. 37, no. 4, pp. 430-447, 2018. Z. Suica, P. Platteau-Waldmeier, S. Koppel, A. Schmidt-Trucksaess, T. Ettlin, and C. Schuster-Amft, "Motor imagery ability assessments in four disciplines: protocol for a systematic review," BMJ Open, vol. 8, no. 12, 2018. L. Power, H. F. Neyedli, S. G. Boe, and T. Bardouille, "E cacy of low-cost wireless neurofeedback to modulate brain activity during motor imagery," Biomedical Physics & Engineering Express, vol. 6, no. 3, p. 035024, 2020. N. Tiwari, D. R. Edla, S. Dodia, and A. Bablani, "Brain computer interface: A comprehensive survey," Biologically Inspired Cognitive Architectures, vol. 26, pp. 118-129, 2018. M. Z. Baig, N. Aslam, and H. P. H. Shum, "Filtering techniques for channel selection in motor imagery EEG applications: a survey," Arti cial Intelligence Review, pp. 1-26, 2019. M. L. Stavrinou, L. Moraru, L. Cimponeriu, S. Della Penna, and A. Bezerianos, "Evaluation of cortical connectivity during real and imagined rhythmic nger tapping," Brain Topography, vol. 19, no. 3, pp. 137-145, 2007. L. S. Imperatori, M. Betta, L. Cecchetti, A. Canales-Johnson, E. Ricciardi, F. Siclari, P. Pietrini, S. Chennu, and G. Bernardi, "Eeg functional connectivity metrics wpli and wsmi account for distinct types of brain functional interactions," Scienti c reports, vol. 9, no. 1, pp. 1-15, 2019. B. Juan, E. Santiago, B.-M. Arturo, N. Marius, B. Javier, F. Eduardo, S. Surjo, and G.-A. Nicolas, "Synchronization of slow cortical rhythms during motor imagery-based brain-machine interface control," International Journal of neural systems, vol. 29, no. 5, p. 1850045, 2019. P. Wierzga la, D. Zapa la, G. M. Wojcik, and J. Masiak, "Most popular signal processing methods in motor-imagery bci: a review and meta-analysis," Frontiers in neuroinformatics, vol. 12, p. 78, 2018. E. Friedrich, R. Scherer, and C. Neuper, "Stability of event-related (de-) synchronization during brain-computer interface-relevant mental tasks," Clinical Neurophysiology, vol. 124, no. 1, pp. 61-69, 2013. P. K. Pattnaik and J. Sarraf, "Brain computer interface issues on hand movement," Computer and Information Sciences, vol. 30, no. 1, pp. 18-24, 2018. Y. Park and W. Chung, "Frequency-optimized local region common spatial pattern approach for motor imagery classi cation," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 7, pp. 1378-1388, 2019. S. Saha, K. I. U. Ahmed, R. Mostafa, L. Hadjileontiadis, and A. Khandoker, "Evidence of variabilities in EEG dynamics during motor imagery-based multiclass brain-computer interface," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 2, pp. 371-382, 2018. M. Ahn and S. C. Jun, "Performance variation in motor imagery brain-computer interface: a brief review," Journal of neuroscience methods, vol. 243, pp. 103-110, 2015. C. Jeunet, S. Debener, F. Lotte, J. Mattout, R. Scherer, and C. Zich, "Mind the traps! design guidelines for rigorous BCI experiments," in Brain-Computer Interfaces Handbook. CRC Press, 2018, pp. 639-660. M. C. Thompson, "Critiquing the concept of bci illiteracy," Science and engineering ethics, vol. 25, no. 4, pp. 1217-1233, 2019. T. Liu, G. Huang, N. Jiang, L. Yao, and Z. Zhang, "Reduce brain computer interface ine ciency by combining sensory motor rhythm and movement-related cortical potential features," Journal of neural engineering, vol. 17, no. 3, p. 035003, 2020. P. Durka, D. Ircha, C. Neuper, and G. Pfurtscheller, "Time-frequency microstructure of event-related electro-encephalogram desynchronisation and synchronisation," Medical & biological engineering & computing, vol. 39, pp. 315-321, 2001. R. Grandchamp and A. Delorme, "Single-trial normalization for event-related spectral decomposition reduces sensitivity to noisy trials," Frontiers in Psychology, vol. 2, p. 236, 2011. H. Yuan and B. He, "Brain{computer interfaces using sensorimotor rhythms: current state and future perspectives," IEEE Transactions on Biomedical Engineering, vol. 61, no. 5, pp. 1425-1435, 2014. X. Tang, W. Li, X. Li, W. Ma, and X. Dang, "Motor imagery eeg recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network," Expert Systems with Applications, vol. 149, p. 113285, 2020. L. Gao, J. Wang, and L. Chen, "Event-related desynchronization and synchronization quanti cation in motor-related eeg by kolmogorov entropy," Journal of neural engineering, vol. 10, no. 3, p. 036023, 2013. H. Azami, P. Li, S. Arnold, J. Escudero, and A. Humeau-Heurtier, "Fuzzy entropy metrics for the analysis of biomedical signals: Assessment and comparison," IEEE Access, vol. 7, pp. 104 833-104 847, 2019. W.-Y. Hsu, "Assembling a multi-feature EEG classifier for left-right motor imagery data using wavelet-based fuzzy approximate entropy for improved accuracy," International journal of neural systems, vol. 25 8, p. 1550037, 2015. C. Shunfei, L. Zhizeng, and G. Haitao, "An entropy fusion method for feature extraction of EEG," Neural Computing and Applications, vol. 29, pp. 857-863, 2016. P. Sri, K. Yashasvi, A. Anjum, A. Bhattacharyya, and R. Pachori, "Development of an Efective Computing Framework for Classi cation of Motor Imagery EEG Signals for Brain-Computer Interface". Singapore: Springer Singapore, 2020, pp. 17-35. M. Rostaghi and H. Azami, "Dispersion entropy: A measure for time-series analysis," IEEE Signal Processing Letters, vol. 23, pp. 610-614, 2016. K. Kuntzelman, L. Rhodes, L. Harrington, and V. Miskovic, "A practical comparison of algorithms for the measurement of multiscale entropy in neural time series data," Brain and Cognition, vol. 123, pp. 126-135, 2018. Y. Li, X. Gao, and Wang.L, "Reverse dispersion entropy: A new complexity measure for sensor signal," Sensors (Basel, Switzerland), vol. 19, 2019. E. Kafantaris, I. Piper, M. Lo, and J. Escudero, "Augmentation of dispersion entropy for handling missing and outlier samples in physiological signal monitoring," Entropy, no. 319, pp. 1-26, 2020. E. Pitsik, N. Frolov, K. Hauke, V. Grubov, V. Maksimenko, J. Kurths, and A. Hramov, "Motor execution reduces eeg signals complexity: Recurrence quanti cation analysis study," Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 30, no. 2, p. 023111, 2020. M. Miao, H. Zeng, A. Wang, C. Zhao, and F. Liu, \Discriminative spatial-frequency-temporal feature extraction and classi cation of motor imagery EEG: An sparse regression and weighted naïve bayesian classi fier-based approach," Journal of neuroscience methods, vol. 278, pp. 13-24, 2017. N. Bigdely-Shamlo, G. Ibagon, C. Kothe, and T. Mullen, "Finding the optimal cross-subject EEG data alignment method for analysis and BCI," in 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018, pp. 1110-1115. S. Fazli, S. Dähne, W. Samek, F. Biebmann, and K. Müller, "Learning from more than one data source: Data fusion techniques for sensorimotor rhythm-based brain-computer interfaces," Proceedings of the IEEE, vol. 103, no. 6, pp. 891-906, 2015. G. Lio and P. Boulinguez, "How does sensor-space group blind source separation face inter-individual neuroanatomical variability? insights from a simulation study based on the PALS-B12 atlas," Brain Topography, vol. 31, pp. 62-75, 2016. X. Gong, L. Mao, Y. Liu, and Q. Lin, "A jacobi generalized orthogonal joint diagonalization algorithm for joint blind source separation," IEEE Access, vol. 6, pp. 38 464{38 474, 2018. Y. Zhang, C. S. Nam, G. Zhou, J. Jin, X. Wang, and A. Cichocki, "Temporally constrained sparse group spatial patterns for motor imagery BCI," IEEE Transactions on Cybernetics, vol. 49, no. 9, pp. 3322-3332, 2019 A. de Cheveigné, G. M. D. Liberto, D. Arzounian, D. D. Wong, J. Hjortkjaer, S. Fuglsang, and L. C. Parra, "Multiway canonical correlation analysis of brain data," NeuroImage, vol. 186, pp. 728-740, 2019. R. J. Huster and L. Raud, "A tutorial review on multi-subject decomposition of eeg," Brain topography, vol. 31, no. 1, pp. 3-16, 2018. D. K. Emge, F.-B. Vialatte, G. Dreyfus, and T. Adali, "Independent vector analysis for SSVEP signal enhancement, detection, and topographical mapping," Brain Topography, vol. 31, no. 1, pp. 117-124, 2018. S. Bhinge, R. Mowakeaa, V. D. Calhoun, and T. Adali, "Extraction of time-varying spatiotemporal networks using parameter-tuned constrained iva," IEEE transactions on medical imaging, vol. 38, no. 7, pp. 1715-1725, 2019. R. J. Huster and L. Raud, "A tutorial review on multi-subject decomposition of EEG," Brain Topography, vol. 31, no. 1, pp. 3-16, 2018. D. A. Bridwell, J. F. Cavanagh, A. G. E. Collins, M. D. Nunez, R. Srinivasan, S. Stober, and V. D. Calhoun, "Moving beyond ERP components: A selective review of approaches to integrate EEG and behavior," Frontiers in Human Neuroscience, vol. 12, p. 106, 2018 N. Kriegeskorte, M. Mur, and P. Bandettini, "Representational similarity analysis -connecting the branches of systems neuroscience," Frontiers in Systems Neuroscience, vol. 2, p. 4, 2008. Muller, Vidaurre, Schreuder, Meinecke, M. Bunau, J. Muller, C. Vidaurre, M. Schreuder, F. Meinecke, P. Bunau, and K. Muller, 2017. A. Singh, S. Lal, and H. W. Guesgen, "Architectural review of co-adaptive brain computer interface," in 2017 4th Asia-Paci c World Congress on Computer Science and Engineering (APWC on CSE). IEEE, 2017, pp. 200-207. A. Abu-Rmileh, E. Zakkay, L. Shmuelof, and O. Shriki, "Co-adaptive training improves e cacy of a multi-day eeg-based motor imagery bci training," Frontiers in Human Neuroscience, vol. 13, p. 362, 2019. L. M. Alonso-Valerdi, "Python executable script for estimating two effective parameters to individualize brain-computer interfaces: Individual alpha frequency and neurophysiological predictor," Frontiers in neuroinformatics, vol. 10, p. 22, 2016. N. S. E. M. Noor and H. Ibrahim, "Machine learning algorithms and quantitative electroencephalography predictors for outcome prediction in traumatic brain injury: A systematic review," IEEE Access, vol. 8, pp. 102 075-102 092, 2020. L. A. Weber, T. Ethofer, and A.-C. Ehlis, "Predictors of neurofeedback training outcome: A systematic review," NeuroImage: Clinical, vol. 27, p. 102301, 2020. M. Vukelic and A. Gharabaghi, "Self-regulation of circumscribed brain activity modulates spatially selective and frequency speci fic connectivity of distributed resting state networks," Frontiers in behavioral neuroscience, vol. 9, p. 181, 2015. M. Ahn, H. Cho, S. Ahn, and S. C. Jun, \High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery," PloS one, vol. 8, no. 11, p. e80886, 2013. A. Bamdadian, C. Guan, K. K. Ang, and J. Xu, "The predictive role of pre-cue eeg rhythms on mi-based bci classi cation performance," Journal of neuroscience methods, vol. 235, pp. 138-144, 2014. Acqualagna, Laura and Botrel, Loic and Vidaurre, Carmen and Kübler, Andrea and Blankertz, Benjamin, "Large-scale assessment of a fully automatic co-adaptive motor imagery-based brain-computer interface", PLoS ONE bf, 2016, pp. 1-19. W. Samek, C. Vidaurre, K.-R. Müller, and M. Kawanabe, "Stationary common spatial patterns for brain{computer interfacing," Journal of neural engineering, vol. 9, no. 2, p. 026013, 2012. Kwon, Cho, A. Won, M. Jun, A. Ahn, "Use of both eyes-open and eyes-closed resting states may yield a more robust predictor of motor imagery BCI performance," Electronics MDPI, vol. 9, p. 690, 2020. N. Wan, Vai, F. Rosa, "Resting alpha activity predicts learning ability in alpha neurofeedback," Frontiers in human neuroscience, vol. 8, p. 500, 2014. Solesio-Jofre, Beets, Woolley, Pauwels, M. Chalavi, "Age-dependent modulations of resting state connectivity following motor practice," vol. 10, p. 25, 2018. Corsi, Chavez, Schwartz, George, Hugueville, Kahn, Dupont, Bassett, M. de Vico Fallani, "Functional disconnection of associative cortical areas predicts performance during BCI training," NeuroImage, vol. 209, p. 116500, 2020. C. A. Stefano Filho, T. B. d. S. Costa, L. Uribe, P. Rodrigues, D. Soriano, R. Attux, and G. Castellano, "On the (in) e cacy of motor imagery training without feedback and event-related desynchronizations considerations," Biomedical Physics & Engineering Express, vol. 6, no. 3, p. 035030, 2020. W. Daeglau, Debener, K. Condro, M. Daeglau, F. Wallhoff, S. Debener, I. Condro, C. Kranczioch, and C. Zich, "Challenge accepted? individual performance gains for motor imagery practice with humanoid robotic EEG neurofeedback," Sensors, vol. 20, p. 1620, 2020. L. Velasquez-Martinez, J. Caicedo-Acosta, and G. Castellanos-Dominguez, "Entropy-based estimation of event-related de/synchronization in motor imagery using vector-quantized patterns," Entropy, vol. 22, no. 6, p. 703, 2020. P. Chholak, M. Kurkin, P. Hramov, "Phase-amplitude coupling between mu and gamma-waves to carry motor commands," in Proceedings of the 2019 3rd School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR), Innopolis, Russia, pp. 39-45 , 2019. B. Kim and C. Winstein, "Can neurological biomarkers of brain impairment be used to predict poststroke motor recovery? A systematic review," Neurorehabilitation and neural repair, vol 31, pp. 3-24, 2017. J. Yperman, T. Becker, D. Valkenborg, V. Popescu, N. Hellings, B. V. Wijmeersch, and L. M. Peeters, "Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis," BMC neurology, vol. 20, no. 1, pp. 1-15, 2020. L. D. Fiederer, M. V olker, R. T. Schirrmeister, W. Burgard, J. Boedecker, and T. Ball, "Hybrid brain-computer-interfacing for human-compliant robots: inferring continuous subjective ratings with deep regression," Frontiers in Neurorobotics, vol. 13, p. 76, 2019. A. H. Shahid and M. P. Singh, "A deep learning approach for prediction of parkinson's disease progression," Biomedical Engineering Letters, vol. 10, no. 2, pp. 227-239, 2020. Pandey, K. Pandey, and R. Janghel, "Recent deep learning techniques, challenges and its applications for medical healthcare system: a review," Neural Processing Letters, vol. 50, pp. 1907-1935, 2019. A. S. Aghaei, M. S. Mahanta, and K. N. Plataniotis, "Separable common spatiospectral patterns for motor imagery BCI systems," IEEE Transactions on Biomedical Engineering, vol. 63, no. 1, pp. 15-29, 2016. S. Brandl, K. Muller, and W. Samek, "Alternative CSP approaches for multimodal distributed BCI data," in 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016, pp. 003 742-003 747. A. M. Bastos and J.-M. Schoffelen, "A tutorial review of functional connectivity analysis methods and their interpretational pitfalls," Frontiers in Systems Neuroscience, vol. 9, p. 175, 2016. S. Dai and Q. Wei, "Electrode channel selection based on backtracking search optimization in motor imagery brain-computer interfaces," Journal of integrative neuroscience, vol. 16, no. 3, pp. 241-254, 2017. C. Sannelli, C. Vidaurre, K. Müller, and B. Blankertz, "Common spatial pattern patches-an optimized filter ensemble for adaptive brain-computer interfaces," in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. IEEE, 2010, pp. 4351-4354. A. Delgado-Bonal and A. Marshak, "Approximate entropy and sample entropy: A comprehensive tutorial," Entropy, vol. 21, p. 541, 2019. T. Nguyen and T. Nguyen, "Entropy-constrained maximizing mutual information quantization," ArXiv, 2020. S. Zhao, B. Chen, P. Zhu, and J. Principe, \Fixed budget quantized kernel least mean-square algorithm," Signal Processing, vol. 93, no. 9, pp. 2759-2770, 2013. D. Cardenas-Pena, A. Tobar-Rodriguez, and G. Castellanos-Dominguez, "Adaptive Bayesian label fusion using kernel-based similarity metrics in hippocampus segmentation," Journal of Medical Imaging, vol. 6, no. 1, pp. 1 - 8, 2019. J. I. Padilla-Buritica, J. M. Ferrandez-Vicente, G. A. Castaño, and C. D. AcostaMedina, "Non-stationary group-level connectivity analysis for enhanced interpretability of oddball tasks," Frontiers in Neuroscience, 2020. K. Mikalsen, F. M. Bianchi, C. Soguero-Ruiz, and R. Jenssen, "Time series cluster kernel for learning similarities between multivariate time series with missing data," Pattern Recognition, vol. 76, pp. 569-581, 2018. B. Blankertz, C. Sannelli, S. Halder, E. M. Hammer, A. Kübler, K.-R. Müller, G. Curio, and T. Dickhaus, "Neurophysiological predictor of smr-based bci performance," Neuroimage, vol. 51, no. 4, pp. 1303-1309, 2010 M. K. I. Molla, A. Al Shiam, M. R. Islam, and T. Tanaka, "Discriminative feature selection-based motor imagery classi cation using eeg signal," IEEE Access, vol. 8, pp. 98 255-98 265, 2020 G. Pfurtscheller and F. L. Da Silva, "Event-related eeg/meg synchronization and desynchronization: basic principles," Clinical neurophysiology, vol. 110, no. 11, pp. 1842-1857, 1999. H.-T. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrado, W. Chai, M. Ispir et al., "Wide & deep learning for recommender systems," in Proceedings of the 1st workshop on deep learning for recommender systems, 2016, pp. 7-10. P. Nagabushanam, S. Thomas George, and S. Radha, "Eeg signal classi cation using lstm and improved neural network algorithms," Soft Computing, vol. 24, no. 13, pp. 9981-10 003, 2020. H. Cho, M. Ahn, S. Ahn, M. Kwon, and S. C. Jun, "Eeg datasets for motor imagery brain computer interface," Gigascience, 2017. B. Graimann, J. E. Huggins, S. P. Levine, and G. Pfurtscheller, "Visualization of signi cant ERD/ERS patterns in multichannel EEG and ECoG data," Clinical neurophysiology, vol. 113, no. 1, pp. 43-47, 2002. V. Carmen, S. T. H, and S. Alois, "BioSig: the free and open source software library for biomedical signal processing," Computational intelligence and neuroscience, vol. 2011, 2011. B. Z. Allison and C. Neuper, "Could anyone use a bci?" in Brain-computer interfaces, D. S. Tan and A. Nijholt, Eds. Springer, 2010, pp. 35-54. A. J. Brockmeier, "Learning and exploiting recurrent patterns in neural data," Ph.D. dissertation, Citeseer, 2014. H. Ghaheri and A. Ahmadyfard, "Extracting common spatial patterns from eeg time segments for classifying motor imagery classes in a brain computer interface (bci)," scientiairanica, vol. 20, no. 6, pp. 2061-2072, 2013. Z. Catharina, D. Stefan, K. Cornelia, B. Martin, G. Ingmar, and D. V. Maarten, "Real-time EEG feedback during simultaneous EEG-fMRI identifi es the cortical signature of motor imagery," Neuroimage, vol. 114, pp. 438-447, 2015. I. Daly, S. J. Nasuto, and K. Warwick, "Brain computer interface control via functional connectivity dynamics," Pattern recognition, vol. 45, no. 6, pp. 2123{2136, 2012. E. W. Lang, A. M. Tome, I. R. Keck, J. Gorriz-Saez, and C. G. Puntonet, "Brain connectivity analysis: a short survey," Computational intelligence and neuroscience, vol. 2012, p. 8, 2012. J. Padilla-Buritica, J. Hurtado, and G. Castellanos-Dominguez, "Supervised piecewise network connectivity analysis for enhanced confi dence of auditory oddball tasks," Biomedical Signal Processing and Control, vol. 52, pp. 341-346, 2019. R. Oostenveld, P. Fries, E. Maris, and J.-M. Schoffelen, "Fieldtrip: open source software for advanced analysis of meg, eeg, and invasive electrophysiological data," Computational intelligence and neuroscience, vol. 2011, 2011. M. P. Van Den Heuvel and H. E. H. Pol, "Exploring the brain network: a review on resting-state fMRI functional connectivity," European neuropsychopharmacology, vol. 20, no. 8, pp. 519-534, 2010. T. Hanakawa, M. A. Dimyan, and M. Hallett, Motor planning, imagery, and execution in the distributed motor network: a time-course study with functional MRI," Cerebral cortex, vol. 18, no. 12, pp. 2775-2788, 2008. K. Kasahara, C. S. DaSalla, M. Honda, and T. Hanakawa, "Neuroanatomical correlates of brain-computer interface performance," Neuroimage, vol. 110, pp. 95-100, 2015. R. Scherer and C. Vidaurre, "Motor imagery based brain{computer interfaces," in Smart Wheelchairs and Brain-Computer Interfaces, P. Diez, Ed. Elsevier, 2018, pp. 171-195. K. Y. Haaland, C. L. Elsinger, A. R. Mayer, S. Durgerian, and S. M. Rao, "Motor sequence complexity and performing hand produce differential patterns of hemispheric lateralization," Journal of Cognitive Neuroscience, vol. 16, no. 4, pp. 621-636, 2004. G. Pfurtscheller, C. Neuper, K. Pichler-Zalaudek, G. Edlinger, and F. H. L. da Silva, "Do brain oscillations of different frequencies indicate interaction between cortical areas in humans?" Neuroscience letters, vol. 286, no. 1, pp. 66-68, 2000. J. Lu, D. McFarland, and J. Wolpaw, "Adaptive laplacian filtering for sensorimotor rhythm-based brain-computer interfaces," Journal of neural engineering, vol. 10, no. 1, p. 016002, 2012. Y. Zhang, C. S. Nam, G. Zhou, J. Jin, X. Wang, and A. Cichocki, "Temporally constrained sparse group spatial patterns for motor imagery bci," IEEE transactions on cybernetics, vol. 49, no. 9, pp. 3322-3332, 2018. C.-F. V. Latchoumane, D. Chung, S. Kim, and J. Jeong, "Segmentation and characterization of eeg during mental tasks using dynamical nonstationarity," Proc. Computational Intelligence in Medical and Healthcare (CIMED 2007), Plymouth, England, 2007. M. Ma, L. Guo, K. Su, and D. Liang, "Classi cation of motor imagery eeg signals based on wavelet transform and sample entropy," in 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), March 2017, pp. 905-910. M. Ahn, H. n. Cho, S. Ahn, and S. Jun, "High theta and low alpha powers may be indicative of bci-illiteracy in motor imagery," PloS one, vol. 8, no. 11, 2013. D. Collazos-Huertas, J. Caicedo-Acosta, G. A. Casta~no-Duque, and C. D. AcostaMedina, "Enhanced multiple instance representation using time-frequency atoms in motor imagery classi cation," Frontiers in neuroscience, vol. 14, p. 155, 2020. G. Pfurtscheller, "Eeg event-related desynchronization (erd) and synchronization (ers)," Electroencephalography and Clinical Neurophysiology, vol. 1, no. 103, p. 26, 1997. L. F. Velasquez-Martinez, F. Zapata-Casta~no, J. I. Padilla-Buritica, J. M. F. Vicente, and G. Castellanos-Dominguez, "Group differences in time-frequency relevant patterns for user-independent BCI applications," in International Work-Conference on the Interplay Between Natural and Arti ficial Computation, 2019, pp. 138-145. E. Maris and R. Oostenveld, "Nonparametric statistical testing of eeg-and megdata," Journal of neuroscience methods, vol. 164, no. 1, pp. 177-190, 2007. R. Giusti and G. E. Batista, "An empirical comparison of dissimilarity measures for time series classi cation," in 2013 Brazilian Conference on Intelligent Systems. IEEE, 2013, pp. 82-88. I. Xygonakis, A. Athanasiou, N. Pandria, D. Kugiumtzis, and P. D. Bamidis, "Decoding motor imagery through common spatial pattern filters at the eeg source space," Computational intelligence and neuroscience, vol. 2018, 2018. M. Matsuo, N. Iso, K. Fujiwara, T. Moriuchi, G. Tanaka, S. Honda, D. Matsuda, and T. Higashi, "Cerebral haemodynamics during motor imagery of self-feeding with chopsticks: differences between dominant and non-dominant hand," Somatosensory & Motor Research, vol. 37, no. 1, pp. 6{-3, 2020. Y. Tian, W. Xu, and L. Yang, "Cortical classifi cation with rhythm entropy for error processing in cocktail party environment based on scalp eeg recording," Scienti c reports, vol. 8, no. 1, pp. 1-13, 2018. B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K.-R. Muller, "Optimizing spatial filters for robust eeg single-trial analysis," IEEE Signal processing magazine, vol. 25, no. 1, pp. 41-56, 2007. F. Kaffashi, R. Foglyano, C. Wilson, and K. Loparo, "The effect of time delay on approximate and sample entropy calculations," Physica D: Nonlinear Phenomena, vol. 237, pp. 3069-3074, 2008. M. C. Thompson, "Critiquing the concept of bci illiteracy," Science and engineering ethics, vol. 25, no. 4, pp. 1217-1233, 2019. C. Neuper and G. Pfurtscheller, "Event-related dynamics of cortical rhythms: frequency-specifi c features and functional correlates," International journal of psychophysiology, vol. 43, no. 1, pp. 41-58, 2001. Y. Zhang, G. Zhou, J. Jin, X. Wang, and A. Cichocki, "Optimizing spatial patterns with sparse filter bands for motor-imagery based brain{computer interface," Journal of neuroscience methods, vol. 255, pp. 85-91, 2015. M. Miao, A. Wang, and F. Liu, "A spatial-frequency-temporal optimized feature sparse representation-based classifi cation method for motor imagery eeg pattern recognition," Medical & biological engineering & computing, vol. 55, no. 9, pp. 1589- 1603, 2017. J. Meng and B. He, "Exploring training effect in 42 human subjects using a noninvasive sensorimotor rhythm based online bci," Frontiers in human neuroscience, vol. 13, p. 128, 2019. X. Shu, S. Chen, L. Yao, X. Sheng, D. Zhang, N. Jiang, J. Jia, and X. Zhu, "Fast recognition of bci-ine cient users using physiological features from eeg signals: A screening study of stroke patients," Frontiers in neuroscience, vol. 12, p. 93, 2018.
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Manizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Automática
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería y Arquitectura
dc.publisher.place.spa.fl_str_mv	Manizales, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Manizales
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Castellanos Dominguez, Cesar Germán954ae54839a598cda0bf4ca7d98773d9Velásquez Martínez, Luisa Fernanda170534193d1c583d3337a67272e54ea9Grupo de Control y Procesamiento Digital de Señaleshttps://scholar.google.com.co/citations?user=kzOD4RQAAAAJ&hl=en2023-08-14T20:31:52Z2023-08-14T20:31:52Z2023https://repositorio.unal.edu.co/handle/unal/84556Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/graficas, tablasEvaluation of brain dynamics elicited by motor imagery (MI) tasks can contribute to clinical and learning applications. In this work, we propose four specific improvements for brain motor intention response analysis based on EEG recordings by considering the nonstationarity, nonlinearity of brain signals, inter- and intra-subject variability, aimed to provide physiological interpretability and the distintiveness between subjects neural response. Firstly, to build up the subject-level feature framework, a common representational space, is proposed that encodes the electrode (spatial) contribution, evolving through time and frequency domains. Three feature extraction methods were compared, providing insight into the possible limitations. Secondly, we present an Entropy-based method, termed \textit{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 \textit{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 \textit{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. Thirdly, multi-subject analysis is to make inferences on the group/population level about the properties of MI brain activity. However, intrinsic neurophysiological variability of neural dynamics poses a challenge for devising efficient MI systems. Here, we develop a \textit{time-frequency} model for estimating the spatial relevance of common neural activity across subjects employing an introduced statistical thresholding rule. In deriving multi-subject spatial maps, we present a comparative analysis of three feature extraction methods: \textit{Common Spatial Patterns}, \textit{Functional Connectivity}, and \textit{Event-Related De/Synchronization}. In terms of interpretability, we evaluate the effectiveness in gathering MI data from collective populations by introducing two assumptions: \textit{i}) Non-linear assessment of the similarity between multi-subject data originating the subject-level dynamics; \textit{ii}) Assessment of time-varying brain network responses according to the ranking of individual accuracy performed in distinguishing distinct motor imagery tasks (left-hand versus right-hand). The obtained validation results indicate that the estimated collective dynamics differently reflect the flow of sensorimotor cortex activation, providing new insights into the evolution of MI responses. Lastly, we develop a data-driven estimator, termed {Deep Regression Network} (DRN), which jointly extracts and performs the regression analysis in order to assess the efficiency of the individual brain networks in practicing MI tasks. The proposed double-stage estimator initially learns a pool of deep patterns, extracted from the input data, in order to feed a neural regression model, allowing for infering the distinctiveness between subject assemblies having similar variability. The results, which were obtained on real-world MI data, prove that the DRN estimator fosters pre-training neural desynchronization and initial training synchronization to predict the bi-class accuracy response, thus providing a better understanding of the Brain--Computer Interface inefficiency of subjects. (Texto tomado de la fuente)La evaluación de la dinámica cerebral provocada por las tareas de imaginación motora (\textit{Motor Imagery - MI}) puede contribuir al desarrollo de aplicaciones clínicas y de aprendizaje. En este trabajo, se proponen cuatro mejoras específicas para el an\'lisis de la respuesta de la intención motora cerebral basada en registros de Electroencefalografía (EEG) al considerar la no estacionariedad, la no linealidad de las se\tilde{n}ales cerebrales y la variabilidad inter e intrasujeto, con el objetivo de proporcionar interpretabilidad fisiológica y la discriminación entre la respuesta neuronal de los sujetos. En primer lugar, para construir el marco de características a nivel de sujeto, se propone un espacio de representación común que codifica la contribución del electrodo (espacial) y como esta evoluciona a través de los dominios de tiempo y frecuencia. Tres métodos de extracción de características fueron comparados, proporcionando información sobre las posibles limitaciones. En segundo lugar, se presenta un método basado en Entropía, denominado \textit{VQEnt}, para la estimación de la desincronización relacionada a eventos (\textit{Event-Related De-Synchronization - ERD/S}) utilizando patrones estocásticos cuantificados en un espacio simbólico, con el objetivo de mejorar su discriminabilidad e interpretabilidad fisiol\'gica. El método propuesto construye los antecedentes probabilísticos mediante la evaluación de la similitud gaussiana entre los datos medidos de entrada y su representación cuantificada vectorial reducida. Los resultados de validación en una base de datos de tareas de imaginación bi-clase (mano izquierda y mano derecha) prueban que \textit{VQEnt} contiene símbolos que codifican varias muestras vecinas, proporcionando una precisión similar o incluso mejor que los otros algoritmos basados en estimación de entropía de referencia. Además, las series temporales de ERD/S calculadas son lo suficientemente cercanas a las trayectorias extraídas por el porcentaje de variación de la potencia de la señal EEG y cumplen con el paradigma fisiológico de MI. En individuos alfabetizados en BCI, el estimador \textit{VQEnt} presenta los resultados precisos con una menor cantidad de electrodos colocados en la corteza sensoriomotora, de modo que el conjunto reducido de canales directamente involucrados con el paradigma MI es suficiente para discriminar entre tareas. En tercer lugar, el análisis multisujeto consiste en hacer inferencias a nivel de grupo/población sobre las propiedades de la actividad cerebral de la imaginación motora. Sin embargo, la variabilidad neurofisiológica intrínseca de la dinámica neuronal plantea un desafío para el diseño de sistemas MI eficientes. En este sentido, se presenta un modelo de \textit{tiempo-frecuencia} para estimar la relevancia espacial de la actividad neuronal común entre sujetos empleando una regla de umbral estadística que deriva en mapas espaciales de múltiples sujetos. Se presenta un análisis comparativo de tres métodos de extracción de características: \textit{Patrones espaciales comunes}, \textit{Conectividad funcional} y \textit{De-sincronización relacionada con eventos}. En términos de interpretabilidad, evaluamos la efectividad en la recopilación de datos de MI para multisujetos mediante la introducción de dos suposiciones: \textit{i}) Evaluación no lineal de la similitud entre los datos de múltiples sujetos que originan la dinámica a nivel de sujeto; \textit{ii}) Evaluación de las respuestas de la red cerebral que varían en el tiempo de acuerdo con la clasificación de la precisión individual realizada al distinguir distintas tareas de imaginación motora (mano izquierda versus mano derecha). Los resultados de validación obtenidos indican que la dinámica colectiva estimada refleja de manera diferente el flujo de activación de la corteza sensoriomotora, lo que proporciona nuevos conocimientos sobre la evolución de las respuestas de MI. Por último, se muestra un estimador denominado {Red de regresión profunda} (\textit{Deep Regression Network - DRN}), que extrae y realiza conjuntamente un análisis de regresión para evaluar la eficiencia de las redes cerebrales individuales, de cada sujeto, en la práctica de tareas de MI. El estimador de doble etapa propuesto inicialmente aprende un conjunto de patrones profundos, extraídos de los datos de entrada, para alimentar un modelo de regresión neuronal, lo que permite inferir la distinción entre conjuntos de sujetos que tienen una variabilidad similar. Los resultados, que se obtuvieron con datos MI del mundo real, demuestran que el estimador DRN usa la desincronización neuronal previa al entrenamiento y la sincronización del entrenamiento inicial para predecir la respuesta de precisión bi-clase, proporcionando así una mejor comprensión de la ineficiencia de la respuesta de MI de los sujetos en las Interfaces Cerebro-Computador.DoctoradoDoctor en IngenieríaReconocimiento de PatronesEléctrica, Electrónica, Automatización Y Telecomunicaciones.Sede Manizalesxii, 77 páginasapplication/pdfengUniversidad Nacional de ColombiaManizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - AutomáticaFacultad de Ingeniería y ArquitecturaManizales, ColombiaUniversidad Nacional de Colombia - Sede Manizales000 - Ciencias de la computación, información y obras generalesMotor ImageryEvent-related synchronizationEntropyBrain-computer interfaceBCI inefficiencyRegression networksMulti-subject analysisImaginación MotoraSincronización relacionada a eventosEntropiaInterfaces cerebro computadorIneficiencia en BCIRedes de regresiónAnálisis multi-sujetoDiscriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraintsDiscriminación de estados mentales mediante la extracción de patrones espaciales bajo restricciones de no estacionariedad e independencia de sujetoTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06ImageTexthttp://purl.org/redcol/resource_type/TDB. Babadi, "Neural encoding and decoding," Handbook of Neuroengineering, pp. 1-24, 2020.A. Singh, A. A. Hussain, S. Lal, and H. W. Guesgen, \A comprehensive review on critical issues and possible solutions of motor imagery based electroencephalography brain-computer interface," Sensors, vol. 21, no. 6, p. 2173, 2021.A. M. Alvarez-Meza, L. F. Velasquez-Martinez, and G. Castellanos-Dominguez, "Time-series discrimination using feature relevance analysis in motor imagery classi cation," Neurocomputing, vol. 151, pp. 122-129, 2015.Y. K. Stolbkov, T. R. Moshonkina, I. V. Orlov, E. S. Tomilovskaya, I. B. Kozlovskaya, and Y. P. Gerasimenko, "The neurophysiological correlates of real and imaginary locomotion," Human Physiology, vol. 45, no. 1, pp. 104-114, 2019.B. Fadlallah, S. Seth, A. Keil, and J. Principe, "Quantifying cognitive state from eeg using dependence measures," IEEE Transactions on Biomedical Engineering, vol. 59, no. 10, pp. 2773-2781, 2012.Z. Chen, "A primer on neural signal processing," IEEE Circuits and Systems Magazine, vol. 17, no. 1, pp. 33-50, 2017S. N. Abdulkader, A. Atia, and M.-S. M. Mostafa, "Brain-computer interfacing: Applications and challenges," Egyptian Informatics Journal, vol. 16, no. 2, pp. 213-230, 2015.M. W. Woolrich, A. Baker, H. Luckhoo, H. Mohseni, G. Barnes, M. Brookes, and I. Rezek, "Dynamic state allocation for meg source reconstruction," Neuroimage, vol. 77, pp. 77-92, 2013.C. Guerrero-Mosquera and A. Navia-Vazquez, "Automatic removal of ocular artefacts using adaptive filtering and independent component analysis for electroencephalogram data," IET signal processing, vol. 6, no. 2, pp. 99-106, 2012.M. Saeidi, W. Karwowski, F. V. Farahani, K. Fiok, R. Taiar, P. Hancock, and A. AlJuaid, "Neural decoding of eeg signals with machine learning: a systematic review," Brain Sciences, vol. 11, no. 11, p. 1525, 2021.D. R. Ripoll, "Introducción a la organización del sistema nervioso," in Neurociencia cognitiva. Editorial Médica Panamericana, 2013, pp. 67-110.Y.-H. Liu, L.-F. Lin, C.-W. Chou, Y. Chang, Y.-T. Hsiao, and W.-C. Hsu, "Analysis of electroencephalography event-related desynchronisation and synchronisation induced by lower-limb stepping motor imagery," Journal of Medical and Biological Engineering, vol. 39, no. 1, pp. 54-69, 2019.G. Rodríguez-Bermúdez and P. J. Garcíaa-Laencina, "Automatic and adaptive classi cation of electroencephalographic signals for brain-computer interfaces," Journal of medical systems, vol. 36, no. 1, pp. 51-63, 2012.S. Lemm, B. Blankertz, T. Dickhaus, and K.-R. M uller, "Introduction to machine learning for brain imaging," Neuroimage, vol. 56, no. 2, pp. 387-399, 2011.E. Opsommer, O. Chevalley, and N. Korogod, "Motor imagery for pain and motor function after spinal cord injury: a systematic review," Spinal Cord, vol. 58, pp. 262-274, 2019.T. Machado, A. Carregosa, M. Santos, N. da Silva, and M. Melo, "E cacy 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," Topics in Stroke Rehabilitation, vol. 26, no. 7, pp. 548-553, 2019.G. Aymeric and D. Ursula, \Bene ts of motor imagery for human space flight: A brief review of current knowledge and future applications," Frontiers in Physiology, no. 10, p. 396, 2019.P. Barhoun, I. Fuelscher, E. Kothe, J. He, G. Youssef, P. Enticott, J. Williams, and C. Hyde, "Motor imagery in children with dcd: A systematic and meta-analytic review of hand-rotation task performance," Neuroscience & Biobehavioral Reviews, vol. 99, pp. 282-297, 2019.V. Nicholson, N. Watts, Y. Chani, and J. Keogh, "Motor imagery training improves balance and mobility outcomes in older adults: a systematic review," Journal of Physiotherapy, vol. 65, no. 4, pp. 200 - 207, 2019.T. MacIntyre, M. A. Madan, C, C. Collet, and A. Guillot, "Motor imagery, performance and motor rehabilitation," vol. 240, pp. 141-159, 2018.S. G. Boe and S. N. Kraeutner, "Assessing motor imagery ability through imagery-based learning: An overview and introduction to miscreen, a mobile app for imagery assessment," Imagination, Cognition and Personality, vol. 37, no. 4, pp. 430-447, 2018.Z. Suica, P. Platteau-Waldmeier, S. Koppel, A. Schmidt-Trucksaess, T. Ettlin, and C. Schuster-Amft, "Motor imagery ability assessments in four disciplines: protocol for a systematic review," BMJ Open, vol. 8, no. 12, 2018.L. Power, H. F. Neyedli, S. G. Boe, and T. Bardouille, "E cacy of low-cost wireless neurofeedback to modulate brain activity during motor imagery," Biomedical Physics & Engineering Express, vol. 6, no. 3, p. 035024, 2020.N. Tiwari, D. R. Edla, S. Dodia, and A. Bablani, "Brain computer interface: A comprehensive survey," Biologically Inspired Cognitive Architectures, vol. 26, pp. 118-129, 2018.M. Z. Baig, N. Aslam, and H. P. H. Shum, "Filtering techniques for channel selection in motor imagery EEG applications: a survey," Arti cial Intelligence Review, pp. 1-26, 2019.M. L. Stavrinou, L. Moraru, L. Cimponeriu, S. Della Penna, and A. Bezerianos, "Evaluation of cortical connectivity during real and imagined rhythmic nger tapping," Brain Topography, vol. 19, no. 3, pp. 137-145, 2007.L. S. Imperatori, M. Betta, L. Cecchetti, A. Canales-Johnson, E. Ricciardi, F. Siclari, P. Pietrini, S. Chennu, and G. Bernardi, "Eeg functional connectivity metrics wpli and wsmi account for distinct types of brain functional interactions," Scienti c reports, vol. 9, no. 1, pp. 1-15, 2019.B. Juan, E. Santiago, B.-M. Arturo, N. Marius, B. Javier, F. Eduardo, S. Surjo, and G.-A. Nicolas, "Synchronization of slow cortical rhythms during motor imagery-based brain-machine interface control," International Journal of neural systems, vol. 29, no. 5, p. 1850045, 2019.P. Wierzga la, D. Zapa la, G. M. Wojcik, and J. Masiak, "Most popular signal processing methods in motor-imagery bci: a review and meta-analysis," Frontiers in neuroinformatics, vol. 12, p. 78, 2018.E. Friedrich, R. Scherer, and C. Neuper, "Stability of event-related (de-) synchronization during brain-computer interface-relevant mental tasks," Clinical Neurophysiology, vol. 124, no. 1, pp. 61-69, 2013.P. K. Pattnaik and J. Sarraf, "Brain computer interface issues on hand movement," Computer and Information Sciences, vol. 30, no. 1, pp. 18-24, 2018.Y. Park and W. Chung, "Frequency-optimized local region common spatial pattern approach for motor imagery classi cation," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 7, pp. 1378-1388, 2019.S. Saha, K. I. U. Ahmed, R. Mostafa, L. Hadjileontiadis, and A. Khandoker, "Evidence of variabilities in EEG dynamics during motor imagery-based multiclass brain-computer interface," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 2, pp. 371-382, 2018.M. Ahn and S. C. Jun, "Performance variation in motor imagery brain-computer interface: a brief review," Journal of neuroscience methods, vol. 243, pp. 103-110, 2015.C. Jeunet, S. Debener, F. Lotte, J. Mattout, R. Scherer, and C. Zich, "Mind the traps! design guidelines for rigorous BCI experiments," in Brain-Computer Interfaces Handbook. CRC Press, 2018, pp. 639-660.M. C. Thompson, "Critiquing the concept of bci illiteracy," Science and engineering ethics, vol. 25, no. 4, pp. 1217-1233, 2019.T. Liu, G. Huang, N. Jiang, L. Yao, and Z. Zhang, "Reduce brain computer interface ine ciency by combining sensory motor rhythm and movement-related cortical potential features," Journal of neural engineering, vol. 17, no. 3, p. 035003, 2020.P. Durka, D. Ircha, C. Neuper, and G. Pfurtscheller, "Time-frequency microstructure of event-related electro-encephalogram desynchronisation and synchronisation," Medical & biological engineering & computing, vol. 39, pp. 315-321, 2001.R. Grandchamp and A. Delorme, "Single-trial normalization for event-related spectral decomposition reduces sensitivity to noisy trials," Frontiers in Psychology, vol. 2, p. 236, 2011.H. Yuan and B. He, "Brain{computer interfaces using sensorimotor rhythms: current state and future perspectives," IEEE Transactions on Biomedical Engineering, vol. 61, no. 5, pp. 1425-1435, 2014.X. Tang, W. Li, X. Li, W. Ma, and X. Dang, "Motor imagery eeg recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network," Expert Systems with Applications, vol. 149, p. 113285, 2020.L. Gao, J. Wang, and L. Chen, "Event-related desynchronization and synchronization quanti cation in motor-related eeg by kolmogorov entropy," Journal of neural engineering, vol. 10, no. 3, p. 036023, 2013.H. Azami, P. Li, S. Arnold, J. Escudero, and A. Humeau-Heurtier, "Fuzzy entropy metrics for the analysis of biomedical signals: Assessment and comparison," IEEE Access, vol. 7, pp. 104 833-104 847, 2019.W.-Y. Hsu, "Assembling a multi-feature EEG classifier for left-right motor imagery data using wavelet-based fuzzy approximate entropy for improved accuracy," International journal of neural systems, vol. 25 8, p. 1550037, 2015.C. Shunfei, L. Zhizeng, and G. Haitao, "An entropy fusion method for feature extraction of EEG," Neural Computing and Applications, vol. 29, pp. 857-863, 2016.P. Sri, K. Yashasvi, A. Anjum, A. Bhattacharyya, and R. Pachori, "Development of an Efective Computing Framework for Classi cation of Motor Imagery EEG Signals for Brain-Computer Interface". Singapore: Springer Singapore, 2020, pp. 17-35.M. Rostaghi and H. Azami, "Dispersion entropy: A measure for time-series analysis," IEEE Signal Processing Letters, vol. 23, pp. 610-614, 2016.K. Kuntzelman, L. Rhodes, L. Harrington, and V. Miskovic, "A practical comparison of algorithms for the measurement of multiscale entropy in neural time series data," Brain and Cognition, vol. 123, pp. 126-135, 2018.Y. Li, X. Gao, and Wang.L, "Reverse dispersion entropy: A new complexity measure for sensor signal," Sensors (Basel, Switzerland), vol. 19, 2019.E. Kafantaris, I. Piper, M. Lo, and J. Escudero, "Augmentation of dispersion entropy for handling missing and outlier samples in physiological signal monitoring," Entropy, no. 319, pp. 1-26, 2020.E. Pitsik, N. Frolov, K. Hauke, V. Grubov, V. Maksimenko, J. Kurths, and A. Hramov, "Motor execution reduces eeg signals complexity: Recurrence quanti cation analysis study," Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 30, no. 2, p. 023111, 2020.M. Miao, H. Zeng, A. Wang, C. Zhao, and F. Liu, \Discriminative spatial-frequency-temporal feature extraction and classi cation of motor imagery EEG: An sparse regression and weighted naïve bayesian classi fier-based approach," Journal of neuroscience methods, vol. 278, pp. 13-24, 2017.N. Bigdely-Shamlo, G. Ibagon, C. Kothe, and T. Mullen, "Finding the optimal cross-subject EEG data alignment method for analysis and BCI," in 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018, pp. 1110-1115.S. Fazli, S. Dähne, W. Samek, F. Biebmann, and K. Müller, "Learning from more than one data source: Data fusion techniques for sensorimotor rhythm-based brain-computer interfaces," Proceedings of the IEEE, vol. 103, no. 6, pp. 891-906, 2015.G. Lio and P. Boulinguez, "How does sensor-space group blind source separation face inter-individual neuroanatomical variability? insights from a simulation study based on the PALS-B12 atlas," Brain Topography, vol. 31, pp. 62-75, 2016.X. Gong, L. Mao, Y. Liu, and Q. Lin, "A jacobi generalized orthogonal joint diagonalization algorithm for joint blind source separation," IEEE Access, vol. 6, pp. 38 464{38 474, 2018.Y. Zhang, C. S. Nam, G. Zhou, J. Jin, X. Wang, and A. Cichocki, "Temporally constrained sparse group spatial patterns for motor imagery BCI," IEEE Transactions on Cybernetics, vol. 49, no. 9, pp. 3322-3332, 2019A. de Cheveigné, G. M. D. Liberto, D. Arzounian, D. D. Wong, J. Hjortkjaer, S. Fuglsang, and L. C. Parra, "Multiway canonical correlation analysis of brain data," NeuroImage, vol. 186, pp. 728-740, 2019.R. J. Huster and L. Raud, "A tutorial review on multi-subject decomposition of eeg," Brain topography, vol. 31, no. 1, pp. 3-16, 2018.D. K. Emge, F.-B. Vialatte, G. Dreyfus, and T. Adali, "Independent vector analysis for SSVEP signal enhancement, detection, and topographical mapping," Brain Topography, vol. 31, no. 1, pp. 117-124, 2018.S. Bhinge, R. Mowakeaa, V. D. Calhoun, and T. Adali, "Extraction of time-varying spatiotemporal networks using parameter-tuned constrained iva," IEEE transactions on medical imaging, vol. 38, no. 7, pp. 1715-1725, 2019.R. J. Huster and L. Raud, "A tutorial review on multi-subject decomposition of EEG," Brain Topography, vol. 31, no. 1, pp. 3-16, 2018.D. A. Bridwell, J. F. Cavanagh, A. G. E. Collins, M. D. Nunez, R. Srinivasan, S. Stober, and V. D. Calhoun, "Moving beyond ERP components: A selective review of approaches to integrate EEG and behavior," Frontiers in Human Neuroscience, vol. 12, p. 106, 2018N. Kriegeskorte, M. Mur, and P. Bandettini, "Representational similarity analysis -connecting the branches of systems neuroscience," Frontiers in Systems Neuroscience, vol. 2, p. 4, 2008.Muller, Vidaurre, Schreuder, Meinecke, M. Bunau, J. Muller, C. Vidaurre, M. Schreuder, F. Meinecke, P. Bunau, and K. Muller, 2017.A. Singh, S. Lal, and H. W. Guesgen, "Architectural review of co-adaptive brain computer interface," in 2017 4th Asia-Paci c World Congress on Computer Science and Engineering (APWC on CSE). IEEE, 2017, pp. 200-207.A. Abu-Rmileh, E. Zakkay, L. Shmuelof, and O. Shriki, "Co-adaptive training improves e cacy of a multi-day eeg-based motor imagery bci training," Frontiers in Human Neuroscience, vol. 13, p. 362, 2019.L. M. Alonso-Valerdi, "Python executable script for estimating two effective parameters to individualize brain-computer interfaces: Individual alpha frequency and neurophysiological predictor," Frontiers in neuroinformatics, vol. 10, p. 22, 2016.N. S. E. M. Noor and H. Ibrahim, "Machine learning algorithms and quantitative electroencephalography predictors for outcome prediction in traumatic brain injury: A systematic review," IEEE Access, vol. 8, pp. 102 075-102 092, 2020.L. A. Weber, T. Ethofer, and A.-C. Ehlis, "Predictors of neurofeedback training outcome: A systematic review," NeuroImage: Clinical, vol. 27, p. 102301, 2020.M. Vukelic and A. Gharabaghi, "Self-regulation of circumscribed brain activity modulates spatially selective and frequency speci fic connectivity of distributed resting state networks," Frontiers in behavioral neuroscience, vol. 9, p. 181, 2015.M. Ahn, H. Cho, S. Ahn, and S. C. Jun, \High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery," PloS one, vol. 8, no. 11, p. e80886, 2013.A. Bamdadian, C. Guan, K. K. Ang, and J. Xu, "The predictive role of pre-cue eeg rhythms on mi-based bci classi cation performance," Journal of neuroscience methods, vol. 235, pp. 138-144, 2014.Acqualagna, Laura and Botrel, Loic and Vidaurre, Carmen and Kübler, Andrea and Blankertz, Benjamin, "Large-scale assessment of a fully automatic co-adaptive motor imagery-based brain-computer interface", PLoS ONE bf, 2016, pp. 1-19.W. Samek, C. Vidaurre, K.-R. Müller, and M. Kawanabe, "Stationary common spatial patterns for brain{computer interfacing," Journal of neural engineering, vol. 9, no. 2, p. 026013, 2012.Kwon, Cho, A. Won, M. Jun, A. Ahn, "Use of both eyes-open and eyes-closed resting states may yield a more robust predictor of motor imagery BCI performance," Electronics MDPI, vol. 9, p. 690, 2020.N. Wan, Vai, F. Rosa, "Resting alpha activity predicts learning ability in alpha neurofeedback," Frontiers in human neuroscience, vol. 8, p. 500, 2014.Solesio-Jofre, Beets, Woolley, Pauwels, M. Chalavi, "Age-dependent modulations of resting state connectivity following motor practice," vol. 10, p. 25, 2018.Corsi, Chavez, Schwartz, George, Hugueville, Kahn, Dupont, Bassett, M. de Vico Fallani, "Functional disconnection of associative cortical areas predicts performance during BCI training," NeuroImage, vol. 209, p. 116500, 2020.C. A. Stefano Filho, T. B. d. S. Costa, L. Uribe, P. Rodrigues, D. Soriano, R. Attux, and G. Castellano, "On the (in) e cacy of motor imagery training without feedback and event-related desynchronizations considerations," Biomedical Physics & Engineering Express, vol. 6, no. 3, p. 035030, 2020.W. Daeglau, Debener, K. Condro, M. Daeglau, F. Wallhoff, S. Debener, I. Condro, C. Kranczioch, and C. Zich, "Challenge accepted? individual performance gains for motor imagery practice with humanoid robotic EEG neurofeedback," Sensors, vol. 20, p. 1620, 2020.L. Velasquez-Martinez, J. Caicedo-Acosta, and G. Castellanos-Dominguez, "Entropy-based estimation of event-related de/synchronization in motor imagery using vector-quantized patterns," Entropy, vol. 22, no. 6, p. 703, 2020.P. Chholak, M. Kurkin, P. Hramov, "Phase-amplitude coupling between mu and gamma-waves to carry motor commands," in Proceedings of the 2019 3rd School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR), Innopolis, Russia, pp. 39-45 , 2019.B. Kim and C. Winstein, "Can neurological biomarkers of brain impairment be used to predict poststroke motor recovery? A systematic review," Neurorehabilitation and neural repair, vol 31, pp. 3-24, 2017.J. Yperman, T. Becker, D. Valkenborg, V. Popescu, N. Hellings, B. V. Wijmeersch, and L. M. Peeters, "Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis," BMC neurology, vol. 20, no. 1, pp. 1-15, 2020.L. D. Fiederer, M. V olker, R. T. Schirrmeister, W. Burgard, J. Boedecker, and T. Ball, "Hybrid brain-computer-interfacing for human-compliant robots: inferring continuous subjective ratings with deep regression," Frontiers in Neurorobotics, vol. 13, p. 76, 2019.A. H. Shahid and M. P. Singh, "A deep learning approach for prediction of parkinson's disease progression," Biomedical Engineering Letters, vol. 10, no. 2, pp. 227-239, 2020.Pandey, K. Pandey, and R. Janghel, "Recent deep learning techniques, challenges and its applications for medical healthcare system: a review," Neural Processing Letters, vol. 50, pp. 1907-1935, 2019.A. S. Aghaei, M. S. Mahanta, and K. N. Plataniotis, "Separable common spatiospectral patterns for motor imagery BCI systems," IEEE Transactions on Biomedical Engineering, vol. 63, no. 1, pp. 15-29, 2016.S. Brandl, K. Muller, and W. Samek, "Alternative CSP approaches for multimodal distributed BCI data," in 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016, pp. 003 742-003 747.A. M. Bastos and J.-M. Schoffelen, "A tutorial review of functional connectivity analysis methods and their interpretational pitfalls," Frontiers in Systems Neuroscience, vol. 9, p. 175, 2016.S. Dai and Q. Wei, "Electrode channel selection based on backtracking search optimization in motor imagery brain-computer interfaces," Journal of integrative neuroscience, vol. 16, no. 3, pp. 241-254, 2017.C. Sannelli, C. Vidaurre, K. Müller, and B. Blankertz, "Common spatial pattern patches-an optimized filter ensemble for adaptive brain-computer interfaces," in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. IEEE, 2010, pp. 4351-4354.A. Delgado-Bonal and A. Marshak, "Approximate entropy and sample entropy: A comprehensive tutorial," Entropy, vol. 21, p. 541, 2019.T. Nguyen and T. Nguyen, "Entropy-constrained maximizing mutual information quantization," ArXiv, 2020.S. Zhao, B. Chen, P. Zhu, and J. Principe, \Fixed budget quantized kernel least mean-square algorithm," Signal Processing, vol. 93, no. 9, pp. 2759-2770, 2013.D. Cardenas-Pena, A. Tobar-Rodriguez, and G. Castellanos-Dominguez, "Adaptive Bayesian label fusion using kernel-based similarity metrics in hippocampus segmentation," Journal of Medical Imaging, vol. 6, no. 1, pp. 1 - 8, 2019.J. I. Padilla-Buritica, J. M. Ferrandez-Vicente, G. A. Castaño, and C. D. AcostaMedina, "Non-stationary group-level connectivity analysis for enhanced interpretability of oddball tasks," Frontiers in Neuroscience, 2020.K. Mikalsen, F. M. Bianchi, C. Soguero-Ruiz, and R. Jenssen, "Time series cluster kernel for learning similarities between multivariate time series with missing data," Pattern Recognition, vol. 76, pp. 569-581, 2018.B. Blankertz, C. Sannelli, S. Halder, E. M. Hammer, A. Kübler, K.-R. Müller, G. Curio, and T. Dickhaus, "Neurophysiological predictor of smr-based bci performance," Neuroimage, vol. 51, no. 4, pp. 1303-1309, 2010M. K. I. Molla, A. Al Shiam, M. R. Islam, and T. Tanaka, "Discriminative feature selection-based motor imagery classi cation using eeg signal," IEEE Access, vol. 8, pp. 98 255-98 265, 2020G. Pfurtscheller and F. L. Da Silva, "Event-related eeg/meg synchronization and desynchronization: basic principles," Clinical neurophysiology, vol. 110, no. 11, pp. 1842-1857, 1999.H.-T. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrado, W. Chai, M. Ispir et al., "Wide & deep learning for recommender systems," in Proceedings of the 1st workshop on deep learning for recommender systems, 2016, pp. 7-10.P. Nagabushanam, S. Thomas George, and S. Radha, "Eeg signal classi cation using lstm and improved neural network algorithms," Soft Computing, vol. 24, no. 13, pp. 9981-10 003, 2020.H. Cho, M. Ahn, S. Ahn, M. Kwon, and S. C. Jun, "Eeg datasets for motor imagery brain computer interface," Gigascience, 2017.B. Graimann, J. E. Huggins, S. P. Levine, and G. Pfurtscheller, "Visualization of signi cant ERD/ERS patterns in multichannel EEG and ECoG data," Clinical neurophysiology, vol. 113, no. 1, pp. 43-47, 2002.V. Carmen, S. T. H, and S. Alois, "BioSig: the free and open source software library for biomedical signal processing," Computational intelligence and neuroscience, vol. 2011, 2011.B. Z. Allison and C. Neuper, "Could anyone use a bci?" in Brain-computer interfaces, D. S. Tan and A. Nijholt, Eds. Springer, 2010, pp. 35-54.A. J. Brockmeier, "Learning and exploiting recurrent patterns in neural data," Ph.D. dissertation, Citeseer, 2014.H. Ghaheri and A. Ahmadyfard, "Extracting common spatial patterns from eeg time segments for classifying motor imagery classes in a brain computer interface (bci)," scientiairanica, vol. 20, no. 6, pp. 2061-2072, 2013.Z. Catharina, D. Stefan, K. Cornelia, B. Martin, G. Ingmar, and D. V. Maarten, "Real-time EEG feedback during simultaneous EEG-fMRI identifi es the cortical signature of motor imagery," Neuroimage, vol. 114, pp. 438-447, 2015.I. Daly, S. J. Nasuto, and K. Warwick, "Brain computer interface control via functional connectivity dynamics," Pattern recognition, vol. 45, no. 6, pp. 2123{2136, 2012.E. W. Lang, A. M. Tome, I. R. Keck, J. Gorriz-Saez, and C. G. Puntonet, "Brain connectivity analysis: a short survey," Computational intelligence and neuroscience, vol. 2012, p. 8, 2012.J. Padilla-Buritica, J. Hurtado, and G. Castellanos-Dominguez, "Supervised piecewise network connectivity analysis for enhanced confi dence of auditory oddball tasks," Biomedical Signal Processing and Control, vol. 52, pp. 341-346, 2019.R. Oostenveld, P. Fries, E. Maris, and J.-M. Schoffelen, "Fieldtrip: open source software for advanced analysis of meg, eeg, and invasive electrophysiological data," Computational intelligence and neuroscience, vol. 2011, 2011.M. P. Van Den Heuvel and H. E. H. Pol, "Exploring the brain network: a review on resting-state fMRI functional connectivity," European neuropsychopharmacology, vol. 20, no. 8, pp. 519-534, 2010.T. Hanakawa, M. A. Dimyan, and M. Hallett, Motor planning, imagery, and execution in the distributed motor network: a time-course study with functional MRI," Cerebral cortex, vol. 18, no. 12, pp. 2775-2788, 2008.K. Kasahara, C. S. DaSalla, M. Honda, and T. Hanakawa, "Neuroanatomical correlates of brain-computer interface performance," Neuroimage, vol. 110, pp. 95-100, 2015.R. Scherer and C. Vidaurre, "Motor imagery based brain{computer interfaces," in Smart Wheelchairs and Brain-Computer Interfaces, P. Diez, Ed. Elsevier, 2018, pp. 171-195.K. Y. Haaland, C. L. Elsinger, A. R. Mayer, S. Durgerian, and S. M. Rao, "Motor sequence complexity and performing hand produce differential patterns of hemispheric lateralization," Journal of Cognitive Neuroscience, vol. 16, no. 4, pp. 621-636, 2004.G. Pfurtscheller, C. Neuper, K. Pichler-Zalaudek, G. Edlinger, and F. H. L. da Silva, "Do brain oscillations of different frequencies indicate interaction between cortical areas in humans?" Neuroscience letters, vol. 286, no. 1, pp. 66-68, 2000.J. Lu, D. McFarland, and J. Wolpaw, "Adaptive laplacian filtering for sensorimotor rhythm-based brain-computer interfaces," Journal of neural engineering, vol. 10, no. 1, p. 016002, 2012.Y. Zhang, C. S. Nam, G. Zhou, J. Jin, X. Wang, and A. Cichocki, "Temporally constrained sparse group spatial patterns for motor imagery bci," IEEE transactions on cybernetics, vol. 49, no. 9, pp. 3322-3332, 2018.C.-F. V. Latchoumane, D. Chung, S. Kim, and J. Jeong, "Segmentation and characterization of eeg during mental tasks using dynamical nonstationarity," Proc. Computational Intelligence in Medical and Healthcare (CIMED 2007), Plymouth, England, 2007.M. Ma, L. Guo, K. Su, and D. Liang, "Classi cation of motor imagery eeg signals based on wavelet transform and sample entropy," in 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), March 2017, pp. 905-910.M. Ahn, H. n. Cho, S. Ahn, and S. Jun, "High theta and low alpha powers may be indicative of bci-illiteracy in motor imagery," PloS one, vol. 8, no. 11, 2013.D. Collazos-Huertas, J. Caicedo-Acosta, G. A. Casta~no-Duque, and C. D. AcostaMedina, "Enhanced multiple instance representation using time-frequency atoms in motor imagery classi cation," Frontiers in neuroscience, vol. 14, p. 155, 2020.G. Pfurtscheller, "Eeg event-related desynchronization (erd) and synchronization (ers)," Electroencephalography and Clinical Neurophysiology, vol. 1, no. 103, p. 26, 1997.L. F. Velasquez-Martinez, F. Zapata-Casta~no, J. I. Padilla-Buritica, J. M. F. Vicente, and G. Castellanos-Dominguez, "Group differences in time-frequency relevant patterns for user-independent BCI applications," in International Work-Conference on the Interplay Between Natural and Arti ficial Computation, 2019, pp. 138-145.E. Maris and R. Oostenveld, "Nonparametric statistical testing of eeg-and megdata," Journal of neuroscience methods, vol. 164, no. 1, pp. 177-190, 2007.R. Giusti and G. E. Batista, "An empirical comparison of dissimilarity measures for time series classi cation," in 2013 Brazilian Conference on Intelligent Systems. IEEE, 2013, pp. 82-88.I. Xygonakis, A. Athanasiou, N. Pandria, D. Kugiumtzis, and P. D. Bamidis, "Decoding motor imagery through common spatial pattern filters at the eeg source space," Computational intelligence and neuroscience, vol. 2018, 2018.M. Matsuo, N. Iso, K. Fujiwara, T. Moriuchi, G. Tanaka, S. Honda, D. Matsuda, and T. Higashi, "Cerebral haemodynamics during motor imagery of self-feeding with chopsticks: differences between dominant and non-dominant hand," Somatosensory & Motor Research, vol. 37, no. 1, pp. 6{-3, 2020.Y. Tian, W. Xu, and L. Yang, "Cortical classifi cation with rhythm entropy for error processing in cocktail party environment based on scalp eeg recording," Scienti c reports, vol. 8, no. 1, pp. 1-13, 2018.B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K.-R. Muller, "Optimizing spatial filters for robust eeg single-trial analysis," IEEE Signal processing magazine, vol. 25, no. 1, pp. 41-56, 2007.F. Kaffashi, R. Foglyano, C. Wilson, and K. Loparo, "The effect of time delay on approximate and sample entropy calculations," Physica D: Nonlinear Phenomena, vol. 237, pp. 3069-3074, 2008.M. C. Thompson, "Critiquing the concept of bci illiteracy," Science and engineering ethics, vol. 25, no. 4, pp. 1217-1233, 2019.C. Neuper and G. Pfurtscheller, "Event-related dynamics of cortical rhythms: frequency-specifi c features and functional correlates," International journal of psychophysiology, vol. 43, no. 1, pp. 41-58, 2001.Y. Zhang, G. Zhou, J. Jin, X. Wang, and A. Cichocki, "Optimizing spatial patterns with sparse filter bands for motor-imagery based brain{computer interface," Journal of neuroscience methods, vol. 255, pp. 85-91, 2015.M. Miao, A. Wang, and F. Liu, "A spatial-frequency-temporal optimized feature sparse representation-based classifi cation method for motor imagery eeg pattern recognition," Medical & biological engineering & computing, vol. 55, no. 9, pp. 1589- 1603, 2017.J. Meng and B. He, "Exploring training effect in 42 human subjects using a noninvasive sensorimotor rhythm based online bci," Frontiers in human neuroscience, vol. 13, p. 128, 2019.X. Shu, S. Chen, L. Yao, X. Sheng, D. Zhang, N. Jiang, J. Jia, and X. Zhu, "Fast recognition of bci-ine cient users using physiological features from eeg signals: A screening study of stroke patients," Frontiers in neuroscience, vol. 12, p. 93, 2018.MincienciasBibliotecariosEstudiantesInvestigadoresMaestrosPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84556/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53ORIGINAL1053768739.2023.pdf1053768739.2023.pdfTesis de Doctorado en Ingeniería - Automáticaapplication/pdf27115133https://repositorio.unal.edu.co/bitstream/unal/84556/5/1053768739.2023.pdff3a5715dc69cb0c853062634007f362cMD55THUMBNAIL1053768739.2023.pdf.jpg1053768739.2023.pdf.jpgGenerated Thumbnailimage/jpeg5351https://repositorio.unal.edu.co/bitstream/unal/84556/6/1053768739.2023.pdf.jpgfb6f0bd9f5e095193b8489fb94a2281dMD56unal/84556oai:repositorio.unal.edu.co:unal/845562023-08-15 23:04:09.346Repositorio Institucional Universidad Nacional de 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