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Latin American Workshop on Computational Neuroscience

LAWCN 2019: Computational Neuroscience pp 236–248Cite as

Considerations on the Individualization of Motor Imagery Neurofeedback Training

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1068))

Abstract

Motor imagery (MI), the mental rehearsal of a movement task, is known to activate similar brain areas to the ones related to actual motor execution. Based on this, MI has been used in many brain-computer interface (BCI) applications, ranging from motor rehabilitation to the actual control of external hardware and other devices. Although great improvement has been made in the field, MI-BCIs still face several issues that limit their applicability to the clinical environment. The aim of this work was to improve on the understanding of one of these issues - namely, the inter and intra-subject variability regarding the electroencephalography (EEG) signals produced by MI tasks. EEG data from 10 healthy subjects who underwent 12 hands-MI sessions, without any feedback, were collected. Differently than most current studies in the field, we screened our analysis into small frequency intervals, from 5–26 Hz, at 4 Hz steps. We then computed how often a given (electrode, frequency interval) pair was optimal for BCI classification, across all sessions, attempting to identify features that would maximize reproducibility. Although similar electrodes are most recurrent across all subjects, each participant displayed their own particularities. Also, these individual patterns did not necessarily reflect the traditional locations over the primary sensorimotor cortex of most EEG-MI studies. Furthermore, our classification results suggested that identifying the best spectral intervals, and not just electrodes, is crucial for improving results. Given the reduced number of existing training protocols supporting individualization, we emphasize that considering subjects’ particularities is essential.

Supported by FAPESP (São Paulo Research Foundation; grants 2016/22116-9, 2017/10341-0 and 2013/07559-3) and CNPq (National Council for Scientific and Technological Development; grant no. 142229/2016-4).

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Author information

Authors and Affiliations

  1. Neurophysics Group, “Gleb Wataghin” Institute of Physics, University of Campinas, Campinas, Brazil

    Carlos A. Stefano Filho & Gabriela Castellano

  2. Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil

    Carlos A. Stefano Filho, Romis Attux & Gabriela Castellano

  3. Signal Processing Department, School of Electrical Engineering and Computing, University of Campinas, Campinas, Brazil

    Romis Attux

Authors
  1. Carlos A. Stefano Filho
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  2. Romis Attux
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  3. Gabriela Castellano
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Corresponding author

Correspondence to Carlos A. Stefano Filho .

Editor information

Editors and Affiliations

  1. Federal University of São João Del-Rei, São João Del-Rei, Brazil

    Vinícius Rosa Cota

  2. Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Dante Augusto Couto Barone

  3. Federal University of São João Del-Rei, São João Del-Rei, Brazil

    Diego Roberto Colombo Dias

  4. Federal University of São João Del-Rei, São João Del-Rei, Brazil

    Laila Cristina Moreira Damázio

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Stefano Filho, C.A., Attux, R., Castellano, G. (2019). Considerations on the Individualization of Motor Imagery Neurofeedback Training. In: Cota, V., Barone, D., Dias, D., Damázio, L. (eds) Computational Neuroscience. LAWCN 2019. Communications in Computer and Information Science, vol 1068. Springer, Cham. https://doi.org/10.1007/978-3-030-36636-0_17

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  • DOI: https://doi.org/10.1007/978-3-030-36636-0_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36635-3

  • Online ISBN: 978-3-030-36636-0

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