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  4. Early predictability of grasping movements by neurofunctional representations : a feasibility study
 
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Early predictability of grasping movements by neurofunctional representations : a feasibility study

Citation Link: https://doi.org/10.15480/882.5174
Publikationstyp
Journal Article
Date Issued
2023-05-06
Sprache
English
Author(s)
Jakubowitz, Eike  
Feist, Thekla  
Obermeier, Alina  
Gempfer, Carina  
Hurschler, Christof  
Windhagen, Henning  
Laves, Max-Heinrich  
Institut
Medizintechnische und Intelligente Systeme E-1  
TORE-DOI
10.15480/882.5174
TORE-URI
http://hdl.handle.net/11420/15394
Journal
Applied Sciences (Basel)  
Volume
13
Issue
9
Article Number
5728
Citation
Applied Sciences (Switzerland) 13 (9): 5728 (2023)
Publisher DOI
10.3390/app13095728
Scopus ID
2-s2.0-85159325216
Publisher
Molecular Diversity Preservation International
Human grasping is a relatively fast process and control signals for upper limb prosthetics cannot be generated and processed in a sufficiently timely manner. The aim of this study was to examine whether discriminating between different grasping movements at a cortical level can provide information prior to the actual grasping process, allowing for more intuitive prosthetic control. EEG datasets were captured from 13 healthy subjects who repeatedly performed 16 activities of daily living. Common classifiers were trained on features extracted from the waking-state frequency and total-frequency time domains. Different training scenarios were used to investigate whether classifiers can already be pre-trained by base networks for fine-tuning with data of a target person. A support vector machine algorithm with spatial covariance matrices as EEG signal descriptors based on Riemannian geometry showed the highest balanced accuracy (0.91 ± 0.05 SD) in discriminating five grasping categories according to the Cutkosky taxonomy in an interval from 1.0 s before to 0.5 s after the initial movement. Fine-tuning did not improve any classifier. No significant accuracy differences between the two frequency domains were apparent (p > 0.07). Neurofunctional representations enabled highly accurate discrimination of five different grasping movements. Our results indicate that, for upper limb prosthetics, it is possible to use them in a sufficiently timely manner and to predict the respective grasping task as a discrete category to kinematically prepare the prosthetic hand.
Subjects
activities of daily living
brain–computer interface
electroencephalography
movement decoding
prosthetic control
DDC Class
004: Informatik
570: Biowissenschaften, Biologie
610: Medizin
Funding Organisations
European Union  
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by/4.0/
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