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Delta-Theta Intertrial Phase Coherence Increases During Task Switching in a BCI Paradigm

  • Juan A. BariosEmail author
  • Santiago Ezquerro
  • Arturo Bertomeu-Motos
  • Eduardo Fernandez
  • Marius Nann
  • Surjo R. Soekadar
  • Nicolas Garcia-Aracil
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10338)

Abstract

A broad variety of perceptual, sensorimotor and cognitive operations have shown to be linked to electroencephalographic (eeg) oscillatory activity. For instance, movement preparation or cognitive processing were linked to delta band (1–5 Hz) oscillations. Such link could be exploited in brain-computer interface (bci) paradigms translating modulations of brain activity into control signals of external devices or computers. However, current bcis are often driven by fast rhythmic brain activity, e.g. in the alpha (9–15 Hz) or beta band (15–30 Hz). Introducing slower oscillations, such as delta or theta (4–8 Hz) band activity, might extent the spectrum of bci applications, particularly in the context of bci-related restoration of movements. To detect voluntary modulations of motor cortical activity in such paradign, an active interval during which users are instructed to e.g. imagine hand movements becomes compared to a task-free interval during which users are instructed to relax. We report that cortical oscillations of eeg in delta and theta frequencies clearly synchronize at the onset and at the end of a bci task, what might be a physiological marker for task switching that could be useful for improving bci control. We also found that inter-trial-phase coherence (itpc) significantly increased at the end of reference intervals during which participants were instructed to relax. This may indicate that during initial phases of bci learning, users are actively relaxing, a finding with important implications for monitoring bci learning and control.

Keywords

Slow rhythms EEG Coherence BCI 

Notes

Acknowledgments

This work has been supported by the European Commission through the project AIDE: “Adaptive Multimodal Interfaces to Assist Disabled People in Daily Activities” (Grant agreement no: 645322), through the project HOMEREHAB: “Development of Development of Robotic Technology for Post-Stroke Home Tele-Rehabilitation—Echord++” (Grant agreement no: 601116) and by the Ministry of Economy and Competitiveness through the project DPI2015-70415-C2-2-R.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Juan A. Barios
    • 1
    Email author
  • Santiago Ezquerro
    • 1
  • Arturo Bertomeu-Motos
    • 1
  • Eduardo Fernandez
    • 1
  • Marius Nann
    • 2
  • Surjo R. Soekadar
    • 2
  • Nicolas Garcia-Aracil
    • 1
  1. 1.Biomedical Neuroengineering Research Group (nBio), Systems Engineering and Automation Department of Miguel Hernandez UniversityElcheSpain
  2. 2.Applied Neurotechnology Lab, Department of Psychiatry and Psychotherapy, Institute of Medical Psychology and Behavioral NeurobiologyUniversity Hospital of TuebingenTuebingenGermany

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