Utilizing High Gamma (HG) Band Power Changes as a Control Signal for Non-Invasive BCI

  • M. Smith
  • K. Weaver
  • T. Grabowski
  • F. Darvas
Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)


Current electroencephalography (EEG) Brain-Computer Interface (BCI) methods typically use control signals (P300, modulated slow cortical potentials, mu or beta rhythm) that suffer from a slow time scale, low signal to noise ratio, and/or low spatial resolution. High gamma oscillations (70–150 Hz; HG) are rapidly evolving, spatially localized signals and recent studies have shown that EEG can reliably detect task-related HG power changes. In this chapter, we discuss how we capitalize on EEG resolved HG as a control signal for BCI. We use functional magnetic resonance imaging (fMRI) to impose spatial constraints in an effort to improve the signal to noise ratio across the HG band. The overall combination lends itself to a fast-responding, dynamic BCI.


Motor Imagery Blood Oxygenation Level Dependent Blood Oxygenation Level Dependent Response High Gamma Slow Cortical Potential 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. F. Darvas, D. Pantazis et al., Mapping human brain function with MEG and EEG: methods and validation. Neuroimage 23(Suppl 1), S289–S299 (2004)CrossRefGoogle Scholar
  2. F. Darvas, R. Scherer et al., High gamma mapping using EEG. NeuroImage 49(1), 930–938 (2010)CrossRefGoogle Scholar
  3. K. Kwong, J. Belliveau et al., Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc. Natl. Acad. Sci. 89, 5675–5679 (1992)CrossRefGoogle Scholar
  4. E.C. Leuthardt, G. Schalk et al., A brain-computer interface using electrocorticographic signals in humans. J. Neural Eng. 1(2), 63–71 (2004)CrossRefGoogle Scholar
  5. K.J. Miller, E.C. Leuthardt et al., Spectral changes in cortical surface potentials during motor movement. J. Neurosci. 27(9), 2424–2432 (2007)CrossRefGoogle Scholar
  6. G. Pfurtscheller, EEG event-related desynchronization (ERD) and event-related synchronization (ERS), in Electroencephalography: Basic Principles, Clinical Applications and Related Fields, 4th edn, eds. by E. Niedermeyer, F. H. Lopes da Silva (Williams and Wilkins, Baltimore, 1999), pp. 958–967Google Scholar
  7. G. Pfurtsheller, F.H. Lopes da Silva, Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin. Neurophysiol. 110, 1842–1857 (1999)CrossRefGoogle Scholar
  8. R. Prückl, B.Z. Allison, C. Hintermüller, B. Großwindhager, C. Kapeller, C. Guger, G. Edlinger, Poor performance in SSVEP BCIs: are worse subjects just slower? in 34th Annual International IEEE EMBS Conference of the IEEE Engineering in Medicine and Biology Society (2012)Google Scholar
  9. J.R. Wolpaw, N. Birbaumer et al., Brain-computer interfaces for communication and control. Clin Neurophys 113, 767–791 (2002)CrossRefGoogle Scholar
  10. J.R. Wolpaw, D. Flotzinger et al., Timing of EEG-based cursor control. J. Clin. Neurophysiol. 16, 529–538 (1997)Google Scholar

Copyright information

© The Author(s) 2013

Authors and Affiliations

  1. 1.Department of Neurobiology and BehaviorUniversity of WashingtonSeattleUS
  2. 2.Department of RadiologyUniversity of WashingtonSeattleUS
  3. 3.Department of NeurologyUniversity of WashingtonSeattleUS
  4. 4.Department of Neurological SurgeryUniversity of WashingtonSeattleUS

Personalised recommendations