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.
Highlights
Grasping is one of the most fundamental ways humans interact with the world, allowing us to manipulate and interact with objects around us
Movement synergies were calculated as the principal components (PCs) of joint angular velocities across all grasp types (Santello et al, 1998; Vinjamuri et al, 2010)
Delta-Band Time Domain EEG Encodes Grasping Kinematics Recent studies on monkeys and humans attempted to decode various aspects of grasping such as joint angles or grasp types from brain activity recorded through microelectrode arrays implanted in the brain or electrocorticographic (ECoG) grids placed over the cortex (Artemiadis et al, 2007; Hamed et al, 2007; Aggarwal et al, 2008; Kubánek et al, 2009; Acharya et al, 2010; Saleh et al, 2010; Vargas-Irwin et al, 2010; Zhuang et al, 2010; Agashe and Contreras-Vidal, 2011; Townsend et al, 2011; Pistohl et al, 2012)
Summary
Grasping is one of the most fundamental ways humans interact with the world, allowing us to manipulate and interact with objects around us. Proximal and distal upper extremity movement information has been shown to be encoded as the EEG predicts hand grasping shape power in various frequency bands in cortical field potentials at various spatial scales, such as local field potentials (LFPs), electrocorticography (ECoG), electroencephalography (EEG), and magnetoencephalography (MEG) (Ball et al, 2008; Kubánek et al, 2009; Waldert et al, 2009; Zhuang et al, 2010; Pistohl et al, 2012). It remains unclear if these amplitude modulations contain enough information to be able to infer the dexterous movement of the fingers during grasping, at the macro scale of scalp EEG
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