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).
Original language | English (US) |
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Pages (from-to) | 132-135 |
Number of pages | 4 |
Journal | Experimental Brain Research |
Volume | 167 |
Issue number | 1 |
DOIs | |
State | Published - Nov 2005 |
Bibliographical note
Funding Information: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.
Keywords
- Copying
- Hand movement
- MEG
- Magnetoencephalography