Brain Topography

, Volume 12, Issue 3, pp 177–186 | Cite as

Mu and Beta Rhythm Topographies During Motor Imagery and Actual Movements

  • Dennis J. McFarland
  • Laurie A. Miner
  • Theresa M. Vaughan
  • Jonathan R. Wolpaw


People can learn to control the 8-12 Hz mu rhythm and/or the 18-25 Hz beta rhythm in the EEG recorded over sensorimotor cortex and use it to control a cursor on a video screen. Subjects often report using motor imagery to control cursor movement, particularly early in training. We compared in untrained subjects the EEG topographies associated with actual hand movement to those associated with imagined hand movement. Sixty-four EEG channels were recorded while each of 33 adults moved left- or right-hand or imagined doing so. Frequency-specific differences between movement or imagery and rest, and between right- and left-hand movement or imagery, were evaluated by scalp topographies of voltage and r spectra, and principal component analysis. Both movement and imagery were associated with mu and beta rhythm desynchronization. The mu topographies showed bilateral foci of desynchronization over sensorimotor cortices, while the beta topographies showed peak desynchronization over the vertex. Both mu and beta rhythm left/right differences showed bilateral central foci that were stronger on the right side. The independence of mu and beta rhythms was demonstrated by differences for movement and imagery for the subjects as a group and by principal components analysis. The results indicated that the effects of imagery were not simply an attenuated version of the effects of movement. They supply evidence that motor imagery could play an important role in EEG-based communication, and suggest that mu and beta rhythms might provide independent control signals.

Sensorimotor cortex Mu rhythm Beta rhythm EEG Imagery 


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  1. Arroyo, S., Lesser, R., Gordon, B., Uematsu, S., Jackson, D. and Webber, R. Functional significance of the mu rhythm of human cortex: an electrophysiologic study with subdural electrodes. Electroenceph. clin. Neurophysiol., 1993, 87: 76-87.Google Scholar
  2. Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kubler, A., Perelmouter, J., Taub, E. and Flor, H. A spelling device for the paralysed. Nature, 1999, 398: 297-28.Google Scholar
  3. Chatrian, G.E. The mu rhythm. In: G.E. Chatrian and G.C. Lairy (Eds.), The EEG of the Waking Adult: Handbook of Electroencephalography and Clinical Neurophysiology, Vol 6A, Elsevier, Amsterdam, 1976: 46-69.Google Scholar
  4. Chatrian, G.E., Petersen, M.C. and Lazarte, J.A. The blocking of the rolandic wicket rhythm and some central changes related to movement. Electroenceph. clin. Neurophysiol., 1959, 11: 497-510.Google Scholar
  5. Decety, J., Perani, D., Jeannerod, M., Bettinardi, V., Tadary, B., Woods, R., Mazzlotta, J.C. and Fazio, F. Mapping motor representations with positron emission tomography. Nature, 1994, 371: 600-602.Google Scholar
  6. Deiber, M.P., Ibanez, V., Honda, M., Sadato, N., Raman, R. and Hallett, M. Cerebral processes related to visuomotor imagery and generation of simple finger movements studied with positron emission tomography. Neuroimage, 1998, 7: 73-85.Google Scholar
  7. Dewan, A.J. Occipital alpha rhythm, eye position and lens accommodation. Nature, 1967, 214: 975-977.Google Scholar
  8. Farwell, L.A. and Donchin, E. Taking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroenceph. clin. Neurophysiol., 1988, 70: 510-523.Google Scholar
  9. Gaustaut, H. Etude electrocorticographique de la reactivite des rhytmes rolandiques. Rev. Neurol. 1952, 87: 176-182.Google Scholar
  10. Jurgens, E., Rosler, F., Hennighausen, E. and Heil, M. Stimulus-induced gamma oscillations: harmonics of alpha activity? Neuroreport, 1995, 6: 813-816.Google Scholar
  11. Kosslyn, S.M., Behrmann, M. and Jeannerod, M. The cognitive neuroscience of mental imagery. Neuropsychologia, 1995, 33: 1335-1344.Google Scholar
  12. Kuhlman, W.N. Functional topography of the human mu rhythm. Electroenceph. clin. Neurophysiol., 1978, 44: 83-93.Google Scholar
  13. Lang, W., Cheyne, D., Hollinger, P., Gerschlager, W. and Lindinger, G. Electric and magnetic fields of the brain accompanying internal simulation of movement. Cog. Brain Res., 1996, 3: 125-129.Google Scholar
  14. Marple, S.L. Digital spectral analysis with applications. Prentice-Hall, Englewood Cliffs, New Jersey, 1987.Google Scholar
  15. McFarland, D.J., Lefkowicz, A.T. and Wolpaw, J.R. Design and operation of an EEG-based brain-computer interface with digital signal processing technology. Behavioral Research Methods, Instruments and Computers, 1997, 29: 337-345.Google Scholar
  16. McFarland, D.J., Neat, G.W., Read, R.F. and Wolpaw, J.R. An EEG-based method for graded cursor control. Psychobiology, 1993, 21: 77-81.Google Scholar
  17. McFarland, D.J., McCane, L.M., David, S.V. and Wolpaw, J.R. Spatial filter selection for EEG-based communication. Electroenceph. clin. Neurophysiol., 1997, 103: 386-394.Google Scholar
  18. Niedermeyer, E. Alpha rhythms as normal and abnormal phenomena. Int. J. Psychophysiol., 1997, 26: 31-49.Google Scholar
  19. Pfurtscheller, G. Mapping of event-related desyncronization and type of derivation. Electroenceph. clin. Neurophysiol., 1988, 70: 190-193.Google Scholar
  20. Pfurtscheller, G. Functional topography during sensorimotor activation studied with event-related desynchronization. J. Clin. Neurophysiol., 1989, 6: 75-84.Google Scholar
  21. Pfurtscheller, G. EEG event-related desynchronization (ERD) and event-related synchronization (ERS). In: E. Niedermeyer and F.H. Lopes da Silva (Eds.), Electroencephalography: Basic Principles, Clinical Applications and Related Fields, 4th Edition, Williams and Wilkins, Baltimore, 1998: 958-967.Google Scholar
  22. Pfurtscheller, G. and Aranibar, A. Evaluation of event-related desyncronization (ERD) preceding and following voluntary self-paced movement. Electroencephal. clin. Neurophysiol., 1979, 46: 138-146.Google Scholar
  23. Pfurtscheller, G. and Berghold, A. Patterns of cortical activation during planning of voluntary movement. Electroenceph. clin. Neurophysiol., 1989, 72: 250-258.Google Scholar
  24. Pfurtscheller, G., Flotzinger, D. and Kalcher, J. Brain-computer interface: a new communication device for handicapped persons. Journal of Microcomputer Applications, 1993, 16: 293-299.Google Scholar
  25. Pfurtscheller, G. and Neuper, C. Motor imagery activates primary sensorimotor area in humans. Neurosci. Lett. 1997, 239: 65-68.Google Scholar
  26. Pfurtscheller, G., Pregenzer, M. and Neuper, C. Visualization of sensorimotor areas involved in preparation for hand movement based on classification of mu and central beta rhythms in single EEG trials in man. Neurosci. Lett., 1994, 181: 43-46.Google Scholar
  27. Pfurtscheller, G., Stancak, A. and Edinger, G. On the existence of different types of central beta rhythms below 30 Hz. Electroenceph. clin Neurophysiol., 1997, 102: 316-325.Google Scholar
  28. Pfurtscheller, G., Stancak, A. and Neuper, C. Event-related synchronization (ERS) in the alpha band-an electrophysical correlate of cortical idling: a review. Int. J. Psychophysiol., 1996, 24: 39-46.Google Scholar
  29. Porro, C.A., Francescato, M.P., Cettolo, V., Diamond, M.E., Baraldi, P., Zuiani, C., Bazzocchi, M. and Prampero, D.E. Primary motor and sensory cortex activation during motor performance and motor imagery: a functional magnetic resonance imaging study. J. Neurosci. 1996, 16: 7688-7698.Google Scholar
  30. Pulvermuller, F., Lutzenberger, W., Preissl, H. and Birbaumer, N. Motor programming in both hemispheres: an EEG study of the human brain. Neurosci. Lett., 1995, 190: 5-8.Google Scholar
  31. Rao, S.M., Binder, J.R., Bandettini, P.A., Hammeke, T.A., Yetkin, F.Z., Jesmanowicz, A., Lisk, L.M., Morris, G.L., Mueller, W.M., Estlowski, L.D., Wong, E.C., Haughton, V.M. and Hyde, J.S. Functional magnetic resonance imaging of complex human movements. Neurology, 1993, 43: 2311-2318.Google Scholar
  32. Rockstroh, B., Elbert, T., Canavan, A., Lutzenberger, W. and Birbaumer, N. Slow cortical potentials and behaviour. 2nd ed. Baltimore: Urban and Schwarzenberg, Baltimore, 1989.Google Scholar
  33. Roland, P.E., Larsen, B., Lassen, N.A. and Skinhoj, E. Supplementary motor area and other cortical areas in organization of voluntary movements in man. J. Neurophysiol., 1980, 43: 118-136.Google Scholar
  34. Salmelin, R. and Hari, R. Characterization of spontaneous MEG rhythms in healthy adults. Electroenceph. clin. Neurophysiol., 1994, 91: 237-248.Google Scholar
  35. Schupp, H.T., Lutzenberger, W., Birbaumer, N., Miltner, W. and Braun, C. Neurophysiological differences between perception and imagery. Cogn. Brain Res., 1994, 2: 77-86.Google Scholar
  36. Sharbrough, F., Chatrian, G.E., Lesser, R.P., Luders, H., Nuwer, M. and Picton, T.W. American Electroencephalographic Society guidelines for standard electrode position nomenclature. J. clin. Neurophysiol., 1991, 8: 200-202.Google Scholar
  37. Stancak, A. and Pfurtscheller, G. The effects of handedness and type of movement on the contralateral preponderance of mu-rhythm desynchronization. Electroenceph. clin. Neurophysiol., 1996, 99: 174-182.Google Scholar
  38. Stancak, A., Riml, A. and Pfurtscheller, G. The effects of external load on movement-related changes of the sensorimotor EEG rhythms. Electroenceph. clin Neurophysiol., 1997, 102: 495-504.Google Scholar
  39. Sutter, E.E. The brain-response interface: communication through visually guided electrical brain responses. J. Microcomp. Appl., 1992, 15: 31-45.Google Scholar
  40. Vaughan, T.M., Miner, L.A., McFarland, D.J. and Wolpaw, J.R. EEG-based communication: analysis of concurrent EMG activity. Electroenceph. clin Neurophysiol., 1998, 107: 428-433.Google Scholar
  41. Vaughan, T.M., Wolpaw, J.R. and Donchin, E. EEG-based communication: prospects and problems. Trans Rehabil Eng., 1996, 4: 425-430.Google Scholar
  42. Winer, B.J. Statistical Principles in Experimental Design (2nd ed). McGraw-Hill, New York, 1962.Google Scholar
  43. Wolpaw, J.R. and McFarland, D.J. Multichannel EEG-based brain-computer communication. Electroenceph. clin. Neurophysiol., 1994, 90: 444-449.Google Scholar
  44. Wolpaw, J.R., McFarland, D.J. and Cacace, A.T. Preliminary studies for a direct brain-to-computer interface. In: Projects for Persons with Disabilities. IBM Technical Symposium, 1986: 11-20.Google Scholar
  45. Wolpaw, J.R., McFarland, D.J., Neat, G.W. and Forneris, C.A. An EEG-based brain-computer interface for cursor control. Electroenceph. clin. Neurophysiol., 1991, 78: 252-259.Google Scholar

Copyright information

© Human Sciences Press, Inc. 2000

Authors and Affiliations

  • Dennis J. McFarland
    • 1
  • Laurie A. Miner
    • 1
  • Theresa M. Vaughan
    • 1
  • Jonathan R. Wolpaw
    • 1
  1. 1.Wadsworth CenterNew York State Department of Health and State University of New YorkAlbanyUSA

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