Spectral Analysis of EEG Signals of Imagined Hand Twisting for Post-stroke Rehabilitation
Stroke is a leading cause of death and remains a major healthcare burden worldwide. Effective rehabilitation strategy is required to improve motor impairment and functional status of stroke survivors. The imagination of movement is one of the methods that can be used in the therapy of stroke survivo...
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Springer Science and Business Media Deutschland GmbH
2021
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2-s2.0-85097614164 Azmi I.N.; Mansor W.; Ahmad N.F. Spectral Analysis of EEG Signals of Imagined Hand Twisting for Post-stroke Rehabilitation 2021 IFMBE Proceedings 80 10.1007/978-3-030-64610-3_116 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097614164&doi=10.1007%2f978-3-030-64610-3_116&partnerID=40&md5=cc4951c24b98979e5f0b283ad0eda42c Stroke is a leading cause of death and remains a major healthcare burden worldwide. Effective rehabilitation strategy is required to improve motor impairment and functional status of stroke survivors. The imagination of movement is one of the methods that can be used in the therapy of stroke survivors at home to gain full recovery. This paper describes the spectral analysis and 2D topography of EEG signals obtained during actual and imagined hand twisting for stroke rehabilitation. The EEG signals were recorded from thirty-two channels, processed and filtered to remove the unwanted signals. The signal features were then extracted using power spectral density and analysed through EEG 2D topography. The results showed that monitoring the status of brain region during actual and imagined twisting could be performed using eight electrodes. The EEG topography revealed a suitable frequency range to monitor the status of the brain activation area for both cases. © 2021, Springer Nature Switzerland AG. Springer Science and Business Media Deutschland GmbH 16800737 English Conference paper |
author |
Azmi I.N.; Mansor W.; Ahmad N.F. |
spellingShingle |
Azmi I.N.; Mansor W.; Ahmad N.F. Spectral Analysis of EEG Signals of Imagined Hand Twisting for Post-stroke Rehabilitation |
author_facet |
Azmi I.N.; Mansor W.; Ahmad N.F. |
author_sort |
Azmi I.N.; Mansor W.; Ahmad N.F. |
title |
Spectral Analysis of EEG Signals of Imagined Hand Twisting for Post-stroke Rehabilitation |
title_short |
Spectral Analysis of EEG Signals of Imagined Hand Twisting for Post-stroke Rehabilitation |
title_full |
Spectral Analysis of EEG Signals of Imagined Hand Twisting for Post-stroke Rehabilitation |
title_fullStr |
Spectral Analysis of EEG Signals of Imagined Hand Twisting for Post-stroke Rehabilitation |
title_full_unstemmed |
Spectral Analysis of EEG Signals of Imagined Hand Twisting for Post-stroke Rehabilitation |
title_sort |
Spectral Analysis of EEG Signals of Imagined Hand Twisting for Post-stroke Rehabilitation |
publishDate |
2021 |
container_title |
IFMBE Proceedings |
container_volume |
80 |
container_issue |
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doi_str_mv |
10.1007/978-3-030-64610-3_116 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097614164&doi=10.1007%2f978-3-030-64610-3_116&partnerID=40&md5=cc4951c24b98979e5f0b283ad0eda42c |
description |
Stroke is a leading cause of death and remains a major healthcare burden worldwide. Effective rehabilitation strategy is required to improve motor impairment and functional status of stroke survivors. The imagination of movement is one of the methods that can be used in the therapy of stroke survivors at home to gain full recovery. This paper describes the spectral analysis and 2D topography of EEG signals obtained during actual and imagined hand twisting for stroke rehabilitation. The EEG signals were recorded from thirty-two channels, processed and filtered to remove the unwanted signals. The signal features were then extracted using power spectral density and analysed through EEG 2D topography. The results showed that monitoring the status of brain region during actual and imagined twisting could be performed using eight electrodes. The EEG topography revealed a suitable frequency range to monitor the status of the brain activation area for both cases. © 2021, Springer Nature Switzerland AG. |
publisher |
Springer Science and Business Media Deutschland GmbH |
issn |
16800737 |
language |
English |
format |
Conference paper |
accesstype |
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record_format |
scopus |
collection |
Scopus |
_version_ |
1809677895249952768 |