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A Simple, Spectral-Change Based, Electrocorticographic Brain–Computer Interface

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Brain-Computer Interfaces

Part of the book series: The Frontiers Collection ((FRONTCOLL))

Abstract

A brain–computer interface (BCI) requires a strong, reliable signal for effective implementation. A wide range of real-time electrical signals have been used for BCI, ranging from scalp recorded electroencephalography (EEG) (see, for example, [1, 2]) to single neuron recordings (see, for example, [3, 4]. Electrocorticography (ECoG) is an intermediate measure, and refers to the recordings obtained directly from the surface of the brain [5]. Like EEG, ECoG represents a population measure, the electrical potential that results from the sum of the local field potentials resulting from 100,000 s of neurons under a given electrode. However, ECoG is a stronger signal and is not susceptible to the artifacts from skin and muscle activity that can plague EEG recordings. ECoG and EEG also differ in that the phenomena they measure encompass fundamentally different scales. Because ECoG electrodes lie on the cortical surface, and because the dipole fields [7] that produce the cortical potentials fall off rapidly \((V(r)\sim r^{ - 2} )\), the ECoG fundamentally reflects more local processes.

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References

  1. J.R. Wolpaw, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, and T.M. Vaughan, Brain-computer interfaces for communication and control. Clin Neurophysiol, 113(6), 767–791, (2002).

    Article  PubMed  Google Scholar 

  2. J.R. Wolpaw and D.J. McFarland, Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc Natl Acad Sci USA, 101(51), 17849–17854, (2004).

    Article  CAS  PubMed  Google Scholar 

  3. L.R. Hochberg, et al., Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature, 442(7099), 164–171, (2006).

    Article  CAS  PubMed  Google Scholar 

  4. P.R. Kennedy and R.A. Bakay, Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport, 9(8), 1707–1711, (1998).

    Article  CAS  PubMed  Google Scholar 

  5. J.G. Ojemann, E.C. Leuthardt, and K.J. Miller, Brain-machine interface: Restoring neurological function through bioengineering. Clin Neurosurg, 54(28), 134–136, (2007).

    Google Scholar 

  6. K.J. Miller, et al., Spectral changes in cortical surface potentials during motor movement. J Neurosci, 27(9), 2424–2432, (2007).

    Article  CAS  PubMed  Google Scholar 

  7. P.L. Nunez and B.A. Cutillo, Neocortical dynamics and human EEG rhythms, Oxford University Press, New York, pp. xii, 708 p., (1995).

    Google Scholar 

  8. E.C. Leuthardt, K.J. Miller, G. Schalk, R.P. Rao, and J.G. Ojemann, Electrocorticography-based brain computer interface – the Seattle experience. IEEE Trans Neural Syst Rehabil Eng, 14(2), 194–198, (2006).

    Article  PubMed  Google Scholar 

  9. E.C. Leuthardt, G. Schalk, J.R. Wolpaw, J.G. Ojemann, and D.W. Moran, A brain-computer interface using electrocorticographic signals in humans. J Neural Eng, 1(2), 63–71, (2004).

    Article  PubMed  Google Scholar 

  10. G. Schalk, et al., Two-dimensional movement control using electrocorticographic signals in humans. J Neural Eng, 5(1), 75–84, (2008).

    Article  CAS  PubMed  Google Scholar 

  11. E.A. Felton, J.A. Wilson, J.C. Williams, and P.C. Garell, Electrocorticographically controlled brain-computer interfaces using motor and sensory imagery in patients with temporary subdural electrode implants. Report of four cases. J Neurosurg, 106(3), 495–500, (2007).

    Article  PubMed  Google Scholar 

  12. J.A. Wilson, E.A. Felton, P.C. Garell, G. Schalk, and J.C. Williams, ECoG factors underlying multimodal control of a brain-computer interface. IEEE Trans Neural Syst Rehabil Eng, 14(2), 246–250, (2006).

    Article  PubMed  Google Scholar 

  13. K.J. Miller, S. Zanos, E.E. Fetz, M. den Nijs, and J.G. Ojemann, Decoupling the cortical power spectrum reveals real-time representation of individual finger movements in humans. J Neurosci, 29(10), 3132, (2009).

    Article  CAS  PubMed  Google Scholar 

  14. T. Blakely, K.J. Miller, R.P.N. Rao, M.D. Holmes, and J.G. Ojemann, Localization and classification of phonemes using high spatial resolution electrocorticography (ECoG) grids. Proc IEEE Eng Med Biol Soc, 4964–4967, (2008).

    Google Scholar 

  15. G. Schalk, D.J. McFarland, T. Hinterberger, N. Birbaumer, and J.R. Wolpaw, BCI2000: A general-purpose brain-computer interface (BCI) system. IEEE Trans Biomed Eng, 51(6), 1034–1043, (2004).

    Article  PubMed  Google Scholar 

  16. K.J. Miller, et al., Cortical electrode localization from X-rays and simple mapping for electrocorticographic research: The “location on cortex” (LOC) package for MATLAB. J Neurosci Methods, 162(1–2), 303–308, (2007).

    Article  PubMed  Google Scholar 

  17. K.J. Miller, et al., Beyond the gamma band: The role of high-frequency features in movement classification. IEEE Trans Biomed Eng, 55(5), 1634–1637, (2008).

    Article  PubMed  Google Scholar 

  18. F. Aoki, E.E. Fetz, L. Shupe, E. Lettich, and G.A. Ojemann, Increased gamma-range activity in human sensorimotor cortex during performance of visuomotor tasks. Clin Neurophysiol, 110(3), 524–537, (1999).

    Article  CAS  PubMed  Google Scholar 

  19. N.E. Crone, D.L. Miglioretti, B. Gordon, and R.P. Lesser, Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. II. Event-related synchronization in the gamma band. Brain, 121(Pt 12), 2301–2315, (1998).

    Article  PubMed  Google Scholar 

  20. N.E. Crone, et al., Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. I. Alpha and beta event-related desynchronization. Brain, 121(Pt 12), 2271–2299, (1998).

    Article  PubMed  Google Scholar 

  21. G. Pfurtscheller, B. Graimann, J.E. Huggins, S.P. Levine, and L.A. Schuh, Spatiotemporal patterns of beta desynchronization and gamma synchronization in corticographic data during self-paced movement. Clin Neurophysiol, 114(7), 1226–1236, (2003).

    Article  CAS  PubMed  Google Scholar 

  22. G. Pfurtscheller, Event-related desynchronization (erd) and event related synchronization (ERS), Williams and Wilkins, Baltimore, MD, pp. 958–967, (1999).

    Google Scholar 

  23. K.J. Miller, L.B. Sorensen, J.G. Ojemann, M. den Nijs: Power-law scaling in the brain surface electric potential. PLOS Comput Biol., 5(12), e1000609, (2009, Dec).

    Google Scholar 

  24. C. Neuper, R. Scherer, M. Reiner, and G. Pfurtscheller, Imagery of motor actions: Differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. Brain Res, 25(3), 668–677, (2005).

    Google Scholar 

  25. K.J. Miller, et al., Real-time functional brain mapping using electrocorticography. NeuroImage, 37(2), 504–507, (2007).

    Article  PubMed  Google Scholar 

  26. A. Brovelli, J.P. Lachaux, P. Kahane, and D. Boussaoud, High gamma frequency oscillatory activity dissociates attention from intention in the human premotor cortex. NeuroImage, 28(1), 154–164, (2005).

    Article  PubMed  Google Scholar 

  27. R.T. Canolty, et al., High gamma power is phase-locked to theta oscillations in human neocortex. Science, 313(5793), 1626–1628, (2006).

    Article  CAS  PubMed  Google Scholar 

  28. K.J. Miller, et al., Beyond the gamma band: The role of high-frequency features in movement classification. IEEE Trans Biomed Eng, 55(5), 1634, (2008).

    Article  PubMed  Google Scholar 

  29. B. Blankertz, G. Dornhege, M. Krauledat, K.R. Muller, and G. Curio, The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects. Neuroimage, 37(2), 539–550, (2007).

    Article  PubMed  Google Scholar 

  30. S. Lemm, B. Blankertz, G. Curio, and K.R. Muller, Spatio-spectral filters for improving the classification of single trial EEG. IEEE Trans Biomed Eng, 52(9), 1541–1548, (2005).

    Article  PubMed  Google Scholar 

  31. K.R. Muller, et al., Machine learning for real-time single-trial EEG-analysis: From brain-computer interfacing to mental state monitoring. J Neurosci Methods, 167(1), 82–90, (2008).

    Article  PubMed  Google Scholar 

  32. P. Shenoy, K.J. Miller, J.G. Ojemann, and R.P. Rao, Generalized features for electrocorticographic BCIs. IEEE Trans Biomed Eng, 55(1), 273–280, (2008).

    Article  PubMed  Google Scholar 

  33. R. Scherer, B. Graimann, J.E. Huggins, S.P. Levine, and G. Pfurtscheller, Frequency component selection for an ECoG-based brain-computer interface. Biomed Tech (Berl), 48(1–2), 31–36, (2003).

    Article  CAS  Google Scholar 

  34. B. Graimann, J.E. Huggins, S.P. Levine, and G. Pfurtscheller, Visualization of significant ERD/ERS patterns in multichannel EEG and ECoG data. Clin Neurophysiol, 113(1), 43–47, (2002).

    Article  CAS  PubMed  Google Scholar 

  35. T. Blakeley, K.J. Miller, S. Zanos, R.P.N. Rao, J.G. Ojemann: Robust long term control of an electrocorticographic brain computer interface with fixed parameters. Neurosurg Focus, 27(1), E13, (2009, Jul).

    Google Scholar 

  36. K.J. Miller, G.S. Schalk, E.E. Fetz, M. den Nijs, J.G. Ojemann, and R.P.N. Rao: Cortical activity during motor movement, motor imagery, and imagery-based online feedback. Proc Natl Acad Sci, 107(9), 4430–4435, (2010, Mar).

    Google Scholar 

  37. G. Schalk, et al., Two-dimensional movement control using electrocorticographic signals in humans. J Neural Eng, 5(1), 75–84, (2008).

    Article  CAS  PubMed  Google Scholar 

  38. K.J. Miller, et al., Three cases of feature correlation in an electrocorticographic BCI. IEEE Eng Med Biol Soc, 5318–5321, (2008).

    Google Scholar 

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Acknowledgements

Special thanks to Gerwin Schalk for his consistent availability and insight. The patients and staff at Harborview Medical Center contributed invaluably of their time and enthusiasm. Author support includes NSF 0130705 and NIH NS07144.

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Authors and Affiliations

  1. Department of Physics, Neurobiology, and Behavior, University of Washington, Seattle, WA, 98195, USA

    Kai J. Miller

  2. Neurological Surgery, University of Washington, Seattle, WA, 98195, USA

    Jeffrey G. Ojemann

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  1. Kai J. Miller
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  2. Jeffrey G. Ojemann
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Correspondence to Kai J. Miller .

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Editors and Affiliations

  1. Otto Bock HealthCare GmbH, Max-Näder-Str. 15, Duderstadt, 37115, Germany

    Bernhard Graimann

  2. Institut für Semantische Datenanalyse/, Knowledge Discovery, Technische Universität Graz, Krenngasse 37, Graz, 8010, Austria

    Gert Pfurtscheller

  3. Inst. Semantische Datenanalyse, Brain-Computer Interface Labor (BCI), TU Graz, Krenngasse 37/III, Graz, 8010, Austria

    Brendan Allison

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Miller, K.J., Ojemann, J.G. (2009). A Simple, Spectral-Change Based, Electrocorticographic Brain–Computer Interface. In: Graimann, B., Pfurtscheller, G., Allison, B. (eds) Brain-Computer Interfaces. The Frontiers Collection. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02091-9_14

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  • DOI: https://doi.org/10.1007/978-3-642-02091-9_14

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  • Publisher Name: Springer, Berlin, Heidelberg

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  • Online ISBN: 978-3-642-02091-9

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