Magnetoencephalographic signals predict movement trajectory in space

Apostolos P Georgopoulos, Frederick J P Langheim, Arthur C Leuthold, Alexander N. Merkle

Research output: Contribution to journalArticlepeer-review

70 Scopus citations


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).

Original languageEnglish (US)
Pages (from-to)132-135
Number of pages4
JournalExperimental Brain Research
Issue number1
StatePublished - Nov 1 2005


  • Copying
  • Hand movement
  • MEG
  • Magnetoencephalography

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