MEG-based neurofeedback for hand rehabilitation

Background: providing neurofeedback (nf) of motor-related brain activity in a biologically-relevant and intuitive way could maximize the utility of a brain-computer interface (bci) for promoting therapeutic plasticity. we present a bci capable of providing intuitive and direct control of a video-based grasp. methods: utilizing magnetoencephalography's (meg) high temporal and spatial resolution,we recorded sensorimotor rhythms (smr) that were modulated by grasp or rest intentions. smr modulation controlled the grasp aperture of a stop motion video of a human hand. the displayed hand grasp position was driven incrementally towards a closed or opened state and subjects were required to hold the targeted position for a time that was adjusted to change the task difficulty. results: we demonstrated that three individuals with complete hand paralysis due to spinal cord injury (sci) were able to maintain brain-control of closing and opening a virtual hand with an average of 63 % success which was significantly above the average chance rate of 19 %. this level of performance was achieved without pre-training and less than 4 min of calibration. in addition,successful grasp targets were reached in 1.96 ± 0.15 s. subjects performed 200 brain-controlled trials in approximately 30 min excluding breaks. two of the three participants showed a significant improvement in smr indicating that they had learned to change their brain activity within a single session of nf. conclusions: this study demonstrated the utility of a meg-based bci system to provide realistic,efficient,and focused nf to individuals with paralysis with the goal of using nf to induce neuroplasticity. © 2015 foldes et al.