Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography
Brain- 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 simu...
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| Format: | Article |
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MDPI AG
2020-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/23/6763 |
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| author | Mads Jochumsen Imran Khan Niazi Muhammad Zia ur Rehman Imran Amjad Muhammad Shafique Syed Omer Gilani Asim Waris |
| author_facet | Mads Jochumsen Imran Khan Niazi Muhammad Zia ur Rehman Imran Amjad Muhammad Shafique Syed Omer Gilani Asim Waris |
| author_sort | Mads Jochumsen |
| collection | DOAJ |
| description | Brain- 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. |
| first_indexed | 2024-03-10T14:33:01Z |
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| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:33:01Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-66f6541000f34091a6899657647035cb2023-11-20T22:27:04ZengMDPI AGSensors1424-82202020-11-012023676310.3390/s20236763Decoding Attempted Hand Movements in Stroke Patients Using Surface ElectromyographyMads Jochumsen0Imran Khan Niazi1Muhammad Zia ur Rehman2Imran Amjad3Muhammad Shafique4Syed Omer Gilani5Asim Waris6Department of Health Science and Technology, Aalborg University, 9220 Aalborg Øst, DenmarkDepartment of Health Science and Technology, Aalborg University, 9220 Aalborg Øst, DenmarkFaculty of Rehabilitation and Allied Sciences & Faculty of Engineering and Applied Sciences, Riphah International University, Islamabad 44000, PakistanCentre for Chiropractic Research, New Zealand College of Chiropractic, Auckland 1060, New ZealandFaculty of Rehabilitation and Allied Sciences & Faculty of Engineering and Applied Sciences, Riphah International University, Islamabad 44000, PakistanDepartment of Biomedical Engineering & Sciences, School of Mechanical & Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, PakistanDepartment of Biomedical Engineering & Sciences, School of Mechanical & Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, PakistanBrain- 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.https://www.mdpi.com/1424-8220/20/23/6763strokeEMGbrain-computer interfacemyoelectric controlpattern recognition |
| spellingShingle | Mads Jochumsen Imran Khan Niazi Muhammad Zia ur Rehman Imran Amjad Muhammad Shafique Syed Omer Gilani Asim Waris Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography Sensors stroke EMG brain-computer interface myoelectric control pattern recognition |
| title | Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography |
| title_full | Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography |
| title_fullStr | Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography |
| title_full_unstemmed | Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography |
| title_short | Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography |
| title_sort | decoding attempted hand movements in stroke patients using surface electromyography |
| topic | stroke EMG brain-computer interface myoelectric control pattern recognition |
| url | https://www.mdpi.com/1424-8220/20/23/6763 |
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