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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Main Authors: Floriana Pichiorri, Jlenia Toppi, Valeria de Seta, Emma Colamarino, Marcella Masciullo, Federica Tamburella, Matteo Lorusso, Febo Cincotti, Donatella Mattia
Format: Article
Language:English
Published: BMC 2023-01-01
Series:Journal of NeuroEngineering and Rehabilitation
Subjects:
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.
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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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