Julio José Ibarrola Lacalle-rekin lankidetzan egindako argitalpenak (13)

2023

  1. Applying deep learning in brain computer interface to classify motor imagery

    Journal of Intelligent and Fuzzy Systems, Vol. 45, Núm. 5, pp. 8747-8760

2016

  1. Are low cost Brain Computer Interface headsets ready for motor imagery applications?

    Expert Systems with Applications, Vol. 49, pp. 136-144

  2. Voting Strategy to Enhance Multimodel EEG-Based Classifier Systems for Motor Imagery BCI

    IEEE Systems Journal, Vol. 10, Núm. 3, pp. 1082-1088

2015

  1. Feature selection applying statistical and neurofuzzy methods to EEG-based BCI

    Computational Intelligence and Neuroscience, Vol. 2015

2014

  1. Tuning rules for a quick start up in Dynamic Matrix Control

    ISA Transactions, Vol. 53, Núm. 2, pp. 612-627

2013

  1. How well Fuzzy ARTMAP approximates functions?

    Journal of Intelligent and Fuzzy Systems, Vol. 25, Núm. 2, pp. 335-350

2012

  1. 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

  2. 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

2010

  1. Applying neuro-fuzzy model dFasArt in control systems

    Engineering Applications of Artificial Intelligence, Vol. 23, Núm. 7, pp. 1053-1063

2009

  1. dFasArt: Dynamic neural processing in FasArt model

    Neural Networks, Vol. 22, Núm. 4, pp. 479-487

2008

  1. dNSP: A biologically inspired dynamic Neural network approach to Signal Processing

    Neural Networks, Vol. 21, Núm. 7, pp. 1006-1019

2007

  1. Performance monitoring of closed-loop controlled systems using dFasArt

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2006

  1. A neurofuzzy scheme to on-line identification in an adaptive-predictive control

    Neural Computing and Applications, Vol. 15, Núm. 1, pp. 41-48