Global cortical activity predicts shape of hand during grasping.
Recent studies show that the amplitude of cortical field potentials is modulated in the time domain by grasping kinematics. However, it is unknown if these low frequency modulations persist and contain enough information to decode grasp kinematics in macro-scale activity measured at the scalp via electroencephalography (EEG). Further, it is unclear as to whether joint angle velocities or movement synergies are the optimal kinematics spaces to decode. In this offline decoding study, we infer from human EEG, hand joint angular velocities as well as synergistic trajectories as subjects perform natural reach-to-grasp movements. Decoding accuracy, measured as the correlation coefficient (r) between the predicted and actual movement kinematics, was r = 0.49 ± 0.02 across 15 hand joints. Across the first three kinematic synergies, decoding accuracies were r = 0.59 ± 0.04, 0.47 ± 0.06, and 0.32 ± 0.05. The spatial-temporal pattern of EEG channel recruitment showed early involvement of contralateral frontal-central scalp areas followed by later activation of central electrodes over primary sensorimotor cortical areas. Information content in EEG about the grasp type peaked at 250 ms after movement onset. The high decoding accuracies in this study are significant not only as evidence for time-domain modulation in macro-scale brain activity, but for the field of brain-machine interfaces as well. Our decoding strategy, which harnesses the neural "symphony" as opposed to local members of the neural ensemble (as in intracranial approaches), may provide a means of extracting information about motor intent for grasping without the need for penetrating electrodes and suggests that it may be soon possible to develop non-invasive neural interfaces for the control of prosthetic limbs.
Noninvasive Brain-Machine Interface Systems Lab, Electrical and Computer Engineering, University of Houston Houston, TX, USA.
Harshavardhan A
HA
Agashe
Noninvasive Brain-Machine Interface Systems Lab, Electrical and Computer Engineering, University of Houston Houston, TX, USA.
Andrew Y
AY
Paek
Noninvasive Brain-Machine Interface Systems Lab, Electrical and Computer Engineering, University of Houston Houston, TX, USA ; Hyperspectral Image Analysis Lab, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA.
Yuhang
Y
Zhang
Noninvasive Brain-Machine Interface Systems Lab, Electrical and Computer Engineering, University of Houston Houston, TX, USA.
José L
JL
Contreras-Vidal
brain-machine interfaces; decoding; electroencephalography; grasping