Magnetoencephalographic signals predict movement trajectory in space.
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).
Georgopoulos
Apostolos P
AP
The Domenici Research Center for Mental Illness, Brain Sciences Center (11B), Veterans Affairs Medical Center, One Veterans Drive, Minneapolis, MN 55417, USA. omega@umn.edu
Langheim
Frederick J P
FJ
Leuthold
Arthur C
AC
Merkle
Alexander N
AN
D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D004569:Electroencephalography; D006801:Humans; D015225:Magnetoencephalography; D009068:Movement / Q000502:physiology*; D011237:Predictive Value of Tests; D011597:Psychomotor Performance; D015203:Reproducibility of Results; D013997:Time Factors