José Manuel
Cano Izquierdo
Profesor Titular de Universidad
Publications (40) José Manuel Cano Izquierdo publications
2023
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Applying deep learning in brain computer interface to classify motor imagery
Journal of Intelligent and Fuzzy Systems, Vol. 45, Núm. 5, pp. 8747-8760
2022
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Detecting the Speed Change Intention from EEG Signals: From the Offline and Pseudo-Online Analysis to an Online Closed-Loop Validation
Applied Sciences (Switzerland), Vol. 12, Núm. 1
2021
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Analysis of frequency bands and channels configuration for detecting intention of change speed through EEG
International IEEE/EMBS Conference on Neural Engineering, NER
2018
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GMDH ANN to optimise model development: Prediction of the pressure drop and the heat transfer coefficient during condensation within mini-channels
Applied Thermal Engineering, Vol. 144, pp. 321-330
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Influencia del exoesqueleto de miembro inferior en señales eeg
XXXIX Jornadas de Automática: actas. Badajoz, 5-7 de septiembre de 2018
2016
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Are low cost Brain Computer Interface headsets ready for motor imagery applications?
Expert Systems with Applications, Vol. 49, pp. 136-144
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Voting Strategy to Enhance Multimodel EEG-Based Classifier Systems for Motor Imagery BCI
IEEE Systems Journal, Vol. 10, Núm. 3, pp. 1082-1088
2015
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Feature selection applying statistical and neurofuzzy methods to EEG-based BCI
Computational Intelligence and Neuroscience, Vol. 2015
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Supervised and dynamic neuro-fuzzy systems to classify physiological responses in robot-assisted neurorehabilitation
PLoS ONE, Vol. 10, Núm. 5
2014
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Tuning rules for a quick start up in Dynamic Matrix Control
ISA Transactions, Vol. 53, Núm. 2, pp. 612-627
2013
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How well Fuzzy ARTMAP approximates functions?
Journal of Intelligent and Fuzzy Systems, Vol. 25, Núm. 2, pp. 335-350
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Implementación de una red neuronal para la predicción de la caída de presión en minicanales
VIII Congreso Nacional de Ingeniería Termodinámica. [Recurso electrónico]: libro de actas
2012
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Control loop performance assessment with a dynamic neuro-fuzzy model (dFasArt)
IEEE Transactions on Automation Science and Engineering, Vol. 9, Núm. 2, pp. 377-389
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Improving motor imagery classification with a new BCI design using neuro-fuzzy S-dFasArt
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 20, Núm. 1, pp. 2-7
2011
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Modelling an absorption system assisted by solar energy
Applied Thermal Engineering, Vol. 31, Núm. 1, pp. 112-118
2010
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Applying neuro-fuzzy model dFasArt in control systems
Engineering Applications of Artificial Intelligence, Vol. 23, Núm. 7, pp. 1053-1063
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Maneuver prediction for road vehicles based on a neuro-fuzzy architecture with a low-cost navigation unit
IEEE Transactions on Intelligent Transportation Systems, Vol. 11, Núm. 2, pp. 498-504
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Maneuver prediction for road vehicles based on a novel neuro-fuzzy dynamic architecture
Robotics and Autonomous Systems, Vol. 58, Núm. 12, pp. 1316-1320
2009
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A new metric for supervised dFasArt based on size-dependent scatter matrices that enhances maneuver prediction in road vehicles
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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dFasArt: Dynamic neural processing in FasArt model
Neural Networks, Vol. 22, Núm. 4, pp. 479-487