Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography

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aut.relation.endpage14
aut.relation.issue23
aut.relation.journalSensors (Switzerland)
aut.relation.startpage1
aut.relation.volume20
dc.contributor.authorJochumsen, M
dc.contributor.authorNiazi, IK
dc.contributor.authorRehman, MZU
dc.contributor.authorAmjad, I
dc.contributor.authorShafique, M
dc.contributor.authorGilani, SO
dc.contributor.authorWaris, A
dc.date.accessioned2023-06-13T04:37:57Z
dc.date.available2023-06-13T04:37:57Z
dc.date.issued2020-11-26
dc.description.abstractBrain‐ and muscle‐triggered exoskeletons have been proposed as a means for motor training after a stroke. With the possibility of performing different movement types with an exoskeleton, it is possible to introduce task variability in training. It is difficult to decode different movement types simultaneously from brain activity, but it may be possible from residual muscle activity that many patients have or quickly regain. This study investigates whether nine different motion classes of the hand and forearm could be decoded from forearm EMG in 15 stroke patients. This study also evaluates the test‐retest reliability of a classical, but simple, classifier (linear discriminant analysis) and advanced, but more computationally intensive, classifiers (autoencoders and convolutional neural networks). Moreover, the association between the level of motor impairment and classification accuracy was tested. Three channels of surface EMG were recorded during the following motion classes: Hand Close, Hand Open, Wrist Extension, Wrist Flexion, Supination, Pronation, Lateral Grasp, Pinch Grasp, and Rest. Six repetitions of each motion class were performed on two different days. Hudgins time‐domain features were extracted and classified using linear discriminant analysis and autoencoders, and raw EMG was classified with convolutional neural networks. On average, 79 ± 12% and 80 ± 12% (autoencoders) of the movements were correctly classified for days 1 and 2, respectively, with an intraclass correlation coefficient of 0.88. No association was found between the level of motor impairment and classification accuracy (Spearman correlation: 0.24). It was shown that nine motion classes could be decoded from residual EMG, with autoencoders being the best classification approach, and that the results were reliable across days; this may have implications for the development of EMG‐controlled exoskeletons for training in the patient’s home.
dc.identifier.citationSensors (Switzerland), ISSN: 1424-8220 (Print); 1424-8220 (Online), MDPI AG, 20(23), 1-14. doi: 10.3390/s20236763
dc.identifier.doi10.3390/s20236763
dc.identifier.issn1424-8220
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/10292/16267
dc.languageeng
dc.publisherMDPI AG
dc.relation.urihttps://www.mdpi.com/1424-8220/20/23/6763
dc.rights.accessrightsOpenAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectEMG
dc.subjectbrain-computer interface
dc.subjectmyoelectric control
dc.subjectpattern recognition
dc.subjectstroke
dc.subject46 Information and Computing Sciences
dc.subject4611 Machine Learning
dc.subjectStroke
dc.subjectNeurosciences
dc.subjectBrain Disorders
dc.subjectClinical Research
dc.subjectBioengineering
dc.subjectRehabilitation
dc.subjectNeurological
dc.subjectStroke
dc.subject0301 Analytical Chemistry
dc.subject0502 Environmental Science and Management
dc.subject0602 Ecology
dc.subject0805 Distributed Computing
dc.subject0906 Electrical and Electronic Engineering
dc.subjectAnalytical Chemistry
dc.subject3103 Ecology
dc.subject4008 Electrical engineering
dc.subject4009 Electronics, sensors and digital hardware
dc.subject4104 Environmental management
dc.subject4606 Distributed computing and systems software
dc.subject.meshElectromyography
dc.subject.meshHand
dc.subject.meshHumans
dc.subject.meshMovement
dc.subject.meshReproducibility of Results
dc.subject.meshStroke
dc.subject.meshWrist Joint
dc.subject.meshHand
dc.subject.meshWrist Joint
dc.subject.meshHumans
dc.subject.meshElectromyography
dc.subject.meshReproducibility of Results
dc.subject.meshMovement
dc.subject.meshStroke
dc.subject.meshElectromyography
dc.subject.meshHand
dc.subject.meshHumans
dc.subject.meshMovement
dc.subject.meshReproducibility of Results
dc.subject.meshStroke
dc.subject.meshWrist Joint
dc.titleDecoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography
dc.typeJournal Article
pubs.elements-id508415
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