Exploring high-density corticomuscular networks after stroke to enable a hybrid Brain-Computer Interface for hand motor rehabilitation
Abstract Background Brain-Computer Interfaces (BCI) promote upper limb recovery in stroke patients reinforcing motor related brain activity (from electroencephalogaphy, EEG). Hybrid BCIs which include peripheral signals (electromyography, EMG) as control features could be employed to monitor post-st...
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Format: | Article |
Language: | English |
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BMC
2023-01-01
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Series: | Journal of NeuroEngineering and Rehabilitation |
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Online Access: | https://doi.org/10.1186/s12984-023-01127-6 |
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author | Floriana Pichiorri Jlenia Toppi Valeria de Seta Emma Colamarino Marcella Masciullo Federica Tamburella Matteo Lorusso Febo Cincotti Donatella Mattia |
author_facet | Floriana Pichiorri Jlenia Toppi Valeria de Seta Emma Colamarino Marcella Masciullo Federica Tamburella Matteo Lorusso Febo Cincotti Donatella Mattia |
author_sort | Floriana Pichiorri |
collection | DOAJ |
description | Abstract Background Brain-Computer Interfaces (BCI) promote upper limb recovery in stroke patients reinforcing motor related brain activity (from electroencephalogaphy, EEG). Hybrid BCIs which include peripheral signals (electromyography, EMG) as control features could be employed to monitor post-stroke motor abnormalities. To ground the use of corticomuscular coherence (CMC) as a hybrid feature for a rehabilitative BCI, we analyzed high-density CMC networks (derived from multiple EEG and EMG channels) and their relation with upper limb motor deficit by comparing data from stroke patients with healthy participants during simple hand tasks. Methods EEG (61 sensors) and EMG (8 muscles per arm) were simultaneously recorded from 12 stroke (EXP) and 12 healthy participants (CTRL) during simple hand movements performed with right/left (CTRL) and unaffected/affected hand (EXP, UH/AH). CMC networks were estimated for each movement and their properties were analyzed by means of indices derived ad-hoc from graph theory and compared among groups. Results Between-group analysis showed that CMC weight of the whole brain network was significantly reduced in patients during AH movements. The network density was increased especially for those connections entailing bilateral non-target muscles. Such reduced muscle-specificity observed in patients was confirmed by muscle degree index (connections per muscle) which indicated a connections’ distribution among non-target and contralateral muscles and revealed a higher involvement of proximal muscles in patients. CMC network properties correlated with upper-limb motor impairment as assessed by Fugl-Meyer Assessment and Manual Muscle Test in patients. Conclusions High-density CMC networks can capture motor abnormalities in stroke patients during simple hand movements. Correlations with upper limb motor impairment support their use in a BCI-based rehabilitative approach. |
first_indexed | 2024-04-10T22:49:28Z |
format | Article |
id | doaj.art-821a119c48254cbf9ae6321b3e2fff7c |
institution | Directory Open Access Journal |
issn | 1743-0003 |
language | English |
last_indexed | 2024-04-10T22:49:28Z |
publishDate | 2023-01-01 |
publisher | BMC |
record_format | Article |
series | Journal of NeuroEngineering and Rehabilitation |
spelling | doaj.art-821a119c48254cbf9ae6321b3e2fff7c2023-01-15T12:06:04ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032023-01-0120111410.1186/s12984-023-01127-6Exploring high-density corticomuscular networks after stroke to enable a hybrid Brain-Computer Interface for hand motor rehabilitationFloriana Pichiorri0Jlenia Toppi1Valeria de Seta2Emma Colamarino3Marcella Masciullo4Federica Tamburella5Matteo Lorusso6Febo Cincotti7Donatella Mattia8Neuroelectrical Imaging and Brain Computer Interface Lab, IRCCS Fondazione Santa LuciaNeuroelectrical Imaging and Brain Computer Interface Lab, IRCCS Fondazione Santa LuciaNeuroelectrical Imaging and Brain Computer Interface Lab, IRCCS Fondazione Santa LuciaNeuroelectrical Imaging and Brain Computer Interface Lab, IRCCS Fondazione Santa LuciaNeurology and Neurovascular Treatment Unit, Belcolle HospitalLaboratory of Robotic Neurorehabilitation (NeuroRobot Lab), Neurorehabilitation 1 Department, IRCCS Fondazione Santa LuciaLaboratory of Robotic Neurorehabilitation (NeuroRobot Lab), Neurorehabilitation 1 Department, IRCCS Fondazione Santa LuciaNeuroelectrical Imaging and Brain Computer Interface Lab, IRCCS Fondazione Santa LuciaNeuroelectrical Imaging and Brain Computer Interface Lab, IRCCS Fondazione Santa LuciaAbstract Background Brain-Computer Interfaces (BCI) promote upper limb recovery in stroke patients reinforcing motor related brain activity (from electroencephalogaphy, EEG). Hybrid BCIs which include peripheral signals (electromyography, EMG) as control features could be employed to monitor post-stroke motor abnormalities. To ground the use of corticomuscular coherence (CMC) as a hybrid feature for a rehabilitative BCI, we analyzed high-density CMC networks (derived from multiple EEG and EMG channels) and their relation with upper limb motor deficit by comparing data from stroke patients with healthy participants during simple hand tasks. Methods EEG (61 sensors) and EMG (8 muscles per arm) were simultaneously recorded from 12 stroke (EXP) and 12 healthy participants (CTRL) during simple hand movements performed with right/left (CTRL) and unaffected/affected hand (EXP, UH/AH). CMC networks were estimated for each movement and their properties were analyzed by means of indices derived ad-hoc from graph theory and compared among groups. Results Between-group analysis showed that CMC weight of the whole brain network was significantly reduced in patients during AH movements. The network density was increased especially for those connections entailing bilateral non-target muscles. Such reduced muscle-specificity observed in patients was confirmed by muscle degree index (connections per muscle) which indicated a connections’ distribution among non-target and contralateral muscles and revealed a higher involvement of proximal muscles in patients. CMC network properties correlated with upper-limb motor impairment as assessed by Fugl-Meyer Assessment and Manual Muscle Test in patients. Conclusions High-density CMC networks can capture motor abnormalities in stroke patients during simple hand movements. Correlations with upper limb motor impairment support their use in a BCI-based rehabilitative approach.https://doi.org/10.1186/s12984-023-01127-6Brain computer interfaceEEGEMGCorticomuscolar coherenceStrokeGraph theory |
spellingShingle | Floriana Pichiorri Jlenia Toppi Valeria de Seta Emma Colamarino Marcella Masciullo Federica Tamburella Matteo Lorusso Febo Cincotti Donatella Mattia Exploring high-density corticomuscular networks after stroke to enable a hybrid Brain-Computer Interface for hand motor rehabilitation Journal of NeuroEngineering and Rehabilitation Brain computer interface EEG EMG Corticomuscolar coherence Stroke Graph theory |
title | Exploring high-density corticomuscular networks after stroke to enable a hybrid Brain-Computer Interface for hand motor rehabilitation |
title_full | Exploring high-density corticomuscular networks after stroke to enable a hybrid Brain-Computer Interface for hand motor rehabilitation |
title_fullStr | Exploring high-density corticomuscular networks after stroke to enable a hybrid Brain-Computer Interface for hand motor rehabilitation |
title_full_unstemmed | Exploring high-density corticomuscular networks after stroke to enable a hybrid Brain-Computer Interface for hand motor rehabilitation |
title_short | Exploring high-density corticomuscular networks after stroke to enable a hybrid Brain-Computer Interface for hand motor rehabilitation |
title_sort | exploring high density corticomuscular networks after stroke to enable a hybrid brain computer interface for hand motor rehabilitation |
topic | Brain computer interface EEG EMG Corticomuscolar coherence Stroke Graph theory |
url | https://doi.org/10.1186/s12984-023-01127-6 |
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