Citation: Pei, D.; Olikkal, P.; Adali, T.;
Vinjamuri, R. Reconstructing
Synergy-Based Hand Grasp
Kinematics from Electroencephalo-
graphic Signals. Sensors 2022, 22,
5349. https://doi.org/10.3390/
s22145349
Academic Editors: Enrico Vezzetti,
Gabriele Baronio, Domenico
Speranza, Luca Ulrich and Andrea
Luigi Guerra
Received: 13 June 2022
Accepted: 16 July 2022
Published: 18 July 2022
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Article
Reconstructing Synergy-Based Hand Grasp Kinematics from
Electroencephalographic Signals
Dingyi Pei , Parthan Olikkal , Tülay Adali and Ramana Vinjamuri *
Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County,
Baltimore, MD 21250, USA; dpei1@umbc.edu (D.P.); polikka1@umbc.edu (P.O.); adali@umbc.edu (T.A.)
* Correspondence: rvinjam1@umbc.edu
Abstract:
Brain-machine interfaces (BMIs) have become increasingly popular in restoring the lost
motor function in individuals with disabilities. Several research studies suggest that the CNS may
employ synergies or movement primitives to reduce the complexity of control rather than controlling
each DoF independently, and the synergies can be used as an optimal control mechanism by the
CNS in simplifying and achieving complex movements. Our group has previously demonstrated
neural decoding of synergy-based hand movements and used synergies effectively in driving hand
exoskeletons. In this study, ten healthy right-handed participants were asked to perform six types of
hand grasps representative of the activities of daily living while their neural activities were recorded
using electroencephalography (EEG). From half of the participants, hand kinematic synergies were
derived, and a neural decoder was developed, based on the correlation between hand synergies
and corresponding cortical activity, using multivariate linear regression. Using the synergies and
the neural decoder derived from the first half of the participants and only cortical activities from
the remaining half of the participants, their hand kinematics were reconstructed with an average
accuracy above 70%. Potential applications of synergy-based BMIs for controlling assistive devices in
individuals with upper limb motor deficits, implications of the results in individuals with stroke and
the limitations of the study were discussed.
Keywords: hand kinematics; EEG; neural decoding; hand synergies; brain-machine interfaces
1. Introduction
Globally, stroke and spinal cord injury are the most common causes of upper limb
paralysis. For many of these individuals, the ability to perform simple activities of daily
living (ADLs) may be permanently lost. With extensive rehabilitation, gross arm move-
ments are commonly regained. However, hand paralysis is less responsive to therapy,
often resulting in permanent disability. As high as 60% of individuals with paralysis return
home without functional use of their paretic hand [
1
]. Limitations in intrinsic recovery
from paralysis necessitate the development of neuroprosthetics, wearable exoskeletons
and orthotic devices, and the means to control them to attain optimal performance in
prehension. Researchers including our group have studied synergy-based brain-machine
interfaces (BMIs) and used them in control of hand exoskeletons as a means to offer assistive
devices and rehabilitation [
2
–
4
]. All these studies are based on a hypothesis presented by
Nikolai Bernstein [
5
], where the central nervous system (CNS) simplifies the control and
coordination of the musculoskeletal system with high degrees of freedom (DoFs) in a low
dimensional space using synergies.
Research in human sensorimotor control supports the idea that the CNS simplifies
the control of high-dimensional DoFs and thereby reduces the complexity of motor con-
trol by using synergies [
6
–
8
]. The experimental evidence from human studies suggests
that the hand grasp synergies can be decoded from invasive and noninvasive neural
recordings [9–11].
Transcranial magnetic stimulation (TMS) research has revealed that
Sensors 2022, 22, 5349. https://doi.org/10.3390/s22145349 https://www.mdpi.com/journal/sensors
