Global cortical activity predicts shape of hand during grasping

Abstract

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.

Detecting intention to grasp during reaching movements from EEG

“…In rehabilitation and assistive scenarios, a reliable decoding of the intention to grasp while performing a reaching movement might allow to create more natural upper-limb motor therapies and assistive devices, enabling typical activities of daily living (ADL) as reach-to-grasp tasks. Several works have shown how cortical activity can be used to decode correlates of grasping tasks, with an emphasis on the classification of different grasp types using electrocorticography (ECoG) [8] or EEG [9]. Similarly, one work has shown the possibility of decoding the onset of grasp using ECoG [10].…”

Multisession, noninvasive closed-loop neuroprosthetic control of grasping by upper limb amputees

“…Finger kinematics for a single dof were specified at an 8-bit resolution, with 0 and 255 corresponding to open and fully flexed positions, respectively. Nominally, two synergies of grasping based on PC analysis of the joint angles were identified based on previous work (Agashe and Contreras-Vidal, 2011;Agashe et al, 2015;Santello et al, 1998) corresponding to the correlated movement of the flexion-extension across all fingers and the thumb (PC1), and the thumb rotation (PC2).…”

“…More recently, researchers have shown that information is also encoded in the time-domain amplitudes of these field potentials in the lowest frequency band (0-5 Hz) (Acharya et al, 2010;Bansal et al, 2011;Bradberry et al, 2009Bradberry et al, , 2010Hall et al, 2014;Kubánek et al, 2009;Mollazadeh et al, 2011). In our previous work we showed that grasping movements in able-bodied individuals can be decoded from time-domain low delta band (0.1-1 Hz) electroencephalographic (EEG) activity, a noninvasive modality to record cortical potentials at the scalp Contreras-Vidal, 2011, 2013;Agashe et al, 2015;Paek et al, 2014). Further, we showed that principal components (PCs) of finger kinematics are decoded with the same level of accuracy as finger joint angles during grasp preshaping.…”

“…In a closed-loop BMI scenario, it is advantageous to control the kinematic PCs as they allow grasp preshaping with fewer dof. In our most recent study (Agashe et al, 2015) we demonstrated an amputee's (single session) control over the grasp preshaping by controlling two kinematic PCs in a closed-loop BMI scenario.…”

Estimating wrist joint angle with limited skin deformation information

“…One relies on the classification of biosignal motion patterns, for example, using brain signals for hand motion recognition (Agashe et al, 2015). Less invasive means of measuring biosignal patterns can also be used, for example, on the surface of the skin.…”