Simultaneous decoding of velocity and speed during executed and observed tracking movements: an MEG study

Brain signals carry rich information about voluntary upper-limb movements. Accessing this information to control an end-effector (upper-limb, robotic arm, cursor) has been a central topic in brain-computer interface (BCI) research. To date, non-invasive BCIs based on kinematics decoding have focused on extracting partial information (i.e, single or highly correlated kinematic parameters). In this work, we show that lowfrequency magnetoencephalographic (MEG) signals simultaneously carry information about multiple kinematic parameters. Using linear models, we decoded cursor velocity and speed during executed and observed tracking movements with moderate (0.2 to 0.4) correlation coefficients (CCs). Comparing the CCs between executed and observed tracking movements, revealed that the MEG signals carried more information (0.1 higher CCs) about velocity and speed during the executed tracking movements. The higher correlations were mainly explained by increased predictive activity in primary sensorimotor areas. We could, therefore, show that non-invasive BCIs have the potential to extract multiple kinematic signals from brain activity in sensorimotor areas.

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