Decoding Hand and Cursor Kinematics from Magnetoencephalographic Signals during Tool Use

The ability to decode kinematics of intended movement from neural activity is essential for the development of prosthetic devices, such as artificial arms, that can aid motor-disabled persons. To date, most of the progress in the development of neuromotor prostheses has been obtained by decoding neural activity acquired through invasive means, such as microelectrode arrays seated into motor cortical tissue. In this study, we demonstrate the feasibility of decoding both hand position and velocity from non-invasive magnetoencephalographic signals during a center-out drawing task in familiar and novel environments. The mean correlation coefficients between measured and decoded kinematics ranged from 0.27-0.61 for the horizontal dimension of movement and 0.06-0.58 for the vertical dimension. Our results indicate that non-invasive neuroimaging signals may contain sufficient kinematic information for controlling neuromotor prostheses.