Experimental Brain Research

, Volume 167, Issue 1, pp 132–135 | Cite as

Magnetoencephalographic signals predict movement trajectory in space

  • Apostolos P. Georgopoulos
  • Frederick J. P. Langheim
  • Arthur C. Leuthold
  • Alexander N. Merkle
Research Note

Abstract

Brain-machine interface (BMI) efforts have been focused on using either invasive implanted electrodes or training-extensive conscious manipulation of brain rhythms to control prosthetic devices. Here we demonstrate an excellent prediction of movement trajectory by real-time magnetoencephalography (MEG). Ten human subjects copied a pentagon for 45 s using an X-Y joystick while MEG signals were being recorded from 248 sensors. A linear summation of weighted contributions of the MEG signals yielded a predicted movement trajectory of high congruence to the actual trajectory (median correlation coefficient: r = 0.91 and 0.97 for unsmoothed and smoothed predictions, respectively). This congruence was robust since it remained high in cross-validation analyses (based on the first half of data to predict the second half; median correlation coefficient: r = 0.76 and 0.85 for unsmoothed and smoothed predictions, respectively).

Keywords

Magnetoencephalography MEG Hand movement Copying 

References

  1. Georgopoulos AP, Kettner RE, Schwartz AB (1988) Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population. J Neurosci 8:2928–2937PubMedGoogle Scholar
  2. Hinterberger T, Weiskopf N, Veit R, Wilhelm B, Betta E, Birbaumer N (2004) An EEG-driven brain-computer interface combined with functional magnetic resonance imaging (fMRI). IEEE Trans Biomed Eng 51:971–974CrossRefPubMedGoogle Scholar
  3. Leuthold AC (2003) Subtraction of heart artifact from MEG data: The matched filter revisited. Soc Neurosci Abstr 863.15Google Scholar
  4. McFarland DJ, McCane LM, David SV, Wolpaw JR (1997) Spatial filter selection for EEG-based communication. EEG Clin Neurophysiol 103:386–394CrossRefGoogle Scholar
  5. Scherer R, Muller GR, Neuper C, Graimann B, Pfurtscheller G (2004) An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate. IEEE Trans Biomed Eng 51:979–984CrossRefPubMedGoogle Scholar
  6. Taylor DM, Tillery SI, Schwartz AB (2002) Direct cortical control of 3D neuroprosthetic devices. Science 296:1829–1832CrossRefPubMedGoogle Scholar
  7. Vetter RJ, Williams JC, Hetke JF, Nunamaker EA, Kipke DR (2004) Chronic neural recording using silicon-substrate microelectrode arrays implanted in cerebral cortex. IEEE Trans Biomed Eng 51:896–904CrossRefPubMedGoogle Scholar
  8. Wessberg J, Nicolelis MA (2004) Optimizing a linear algorithm for real-time robotic control using chronic cortical ensemble recordings in monkeys. J Cogn Neurosci 16:1022–1035CrossRefPubMedGoogle Scholar
  9. Wolpaw JR, McFarland DJ (2004) Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc Natl Acad Sci U S A 101:17849–17854PubMedCrossRefGoogle Scholar
  10. Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano LA, Robinson CJ, Vaughan TM (2000) Brain–computer interface technology: a review of the first international meeting. IEEE Trans Rehab Eng 8:164–173CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • Apostolos P. Georgopoulos
    • 1
    • 2
    • 3
    • 4
    • 5
  • Frederick J. P. Langheim
    • 1
    • 2
  • Arthur C. Leuthold
    • 1
    • 2
  • Alexander N. Merkle
    • 1
  1. 1.The Domenici Research Center for Mental Illness, Brain Sciences Center (11B)Veterans Affairs Medical CenterMinneapolisUSA
  2. 2.Department of NeuroscienceUniversity of Minnesota Medical SchoolMinneapolisUSA
  3. 3.Department of NeurologyUniversity of Minnesota Medical SchoolMinneapolisUSA
  4. 4.Department of PsychiatryUniversity of Minnesota Medical SchoolMinneapolisUSA
  5. 5.Center for Cognitive SciencesUniversity of MinnesotaMinneapolisUSA

Personalised recommendations