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).
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Acknowledgements
This work was supported by the MIND Institute (Albuquerque, NM), the U.S. Department of Veterans Affairs, and the American Legion Brain Sciences Chair.
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Georgopoulos, A.P., Langheim, F.J.P., Leuthold, A.C. et al. Magnetoencephalographic signals predict movement trajectory in space. Exp Brain Res 167, 132–135 (2005). https://doi.org/10.1007/s00221-005-0028-8
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DOI: https://doi.org/10.1007/s00221-005-0028-8