Reconstructing Synergy-Based Hand Grasp Kinematics from Electroencephalographic Signals
| dc.contributor.author | Pei, Dingyi | |
| dc.contributor.author | Olikkal, Parthan Sathishkumar | |
| dc.contributor.author | Adali, Tulay | |
| dc.contributor.author | Vinjamuri, Ramana | |
| dc.date.accessioned | 2023-06-06T13:53:12Z | |
| dc.date.available | 2023-06-06T13:53:12Z | |
| dc.date.issued | 2022-07-18 | |
| dc.description.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. | en_US |
| dc.description.sponsorship | This research was funded by National Science Foundation (NSF) CAREER Award, grant number HCC-2053498 and NSF Planning IUCRC Award, grant number 2042203. | en_US |
| dc.description.uri | https://www.mdpi.com/1424-8220/22/14/5349 | en_US |
| dc.format.extent | 16 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.identifier | doi:10.13016/m2pfip-jpz7 | |
| dc.identifier.citation | Pei, Dingyi, Parthan Olikkal, Tülay Adali, and Ramana Vinjamuri. 2022. "Reconstructing Synergy-Based Hand Grasp Kinematics from Electroencephalographic Signals" Sensors 22, no. 14: 5349. https://doi.org/10.3390/s22145349 | en_US |
| dc.identifier.uri | https://doi.org/10.3390/s22145349 | |
| dc.identifier.uri | http://hdl.handle.net/11603/28109 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | MDPI | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | en_US |
| dc.rights | Attribution 4.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/us/ | * |
| dc.title | Reconstructing Synergy-Based Hand Grasp Kinematics from Electroencephalographic Signals | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0001-7756-3678 | en_US |
| dcterms.creator | https://orcid.org/0000-0002-5513-1150 | en_US |
| dcterms.creator | https://orcid.org/0000-0003-0594-2796 | en_US |
| dcterms.creator | https://orcid.org/0000-0003-1650-5524 | en_US |