An eLORETA EEG analysis to spatially resolve real and imagined neuromotor control

BCI (brain computer interface) technology relies on the accurate identification of key anatomical areas to place sensors for effective biofeedback control or monitoring. In motor control applications, these challenges can include identifying brain areas that respond considerably differently to resting state versus limb motion tasks or real motion versus imaginary motion. The eLORETA (exact low resolution brain electromagnetic tomography) source localization algorithm is a potent technique to pinpoint cortical areas using brain signals but it has not yet been applied for motor control applications. In a pilot study, we analyzed human EEG (electroencephalography) during real and imagined movements of upper and lower limbs using the eLORETA algorithm after computing eight selected spectral EEG measures. For certain tasks and movement types, we observed statistically significant differences (p<;.001) in spectral measures. Subsequently, we performed pattern classification to discern real vs. imaginary movement in the eLORETA brain areas as a proof of principle for BCI uses.