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Exploration of neural correlates of movement intention based on characterisation of temporal dependencies in electroencephalography

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  • Maitreyee Wairagkar
  • Yoshikatsu Hayashi
  • Slawomir J Nasuto

Abstract

Brain computer interfaces (BCIs) provide a direct communication channel by using brain signals, enabling patients with motor impairments to interact with external devices. Motion intention detection is useful for intuitive movement-based BCI as movement is the fundamental mode of interaction with the environment. The aim of this paper is to investigate the temporal dynamics of brain processes using electroencephalography (EEG) to explore novel neural correlates of motion intention. We investigate the changes in temporal dependencies of the EEG by characterising the decay of autocorrelation during asynchronous voluntary finger tapping movement. The evolution of the autocorrelation function is characterised by its relaxation time, which is used as a robust marker for motion intention. We observed that there was reorganisation of temporal dependencies in EEG during motion intention. The autocorrelation decayed slower during movement intention and faster during the resting state. There was an increase in temporal dependence during movement intention. The relaxation time of the autocorrelation function showed significant (p

Suggested Citation

  • Maitreyee Wairagkar & Yoshikatsu Hayashi & Slawomir J Nasuto, 2018. "Exploration of neural correlates of movement intention based on characterisation of temporal dependencies in electroencephalography," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-23, March.
    • Handle: RePEc:plo:pone00:0193722
    DOI: 10.1371/journal.pone.0193722
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    References listed on IDEAS

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    1. Ke Liao & Ran Xiao & Jania Gonzalez & Lei Ding, 2014. "Decoding Individual Finger Movements from One Hand Using Human EEG Signals," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-12, January.
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