Research ArticleNo Access

Corticomuscular and Intermuscular Coupling in Simple Hand Movements to Enable a Hybrid Brain–Computer Interface

Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Rome 00185, Italy

Fondazione Santa Lucia IRCCS, Via Ardeatina 306-354, Rome 00179, Italy

E-mail Address: [email protected]

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Valeria de Seta

Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Rome 00185, Italy

Fondazione Santa Lucia IRCCS, Via Ardeatina 306-354, Rome 00179, Italy

E-mail Address: [email protected]

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Marcella Masciullo

Fondazione Santa Lucia IRCCS, Via Ardeatina 306-354, Rome 00179, Italy

E-mail Address: [email protected]

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Febo Cincotti

Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Rome 00185, Italy

Fondazione Santa Lucia IRCCS, Via Ardeatina 306-354, Rome 00179, Italy

E-mail Address: [email protected]

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Donatella Mattia

Fondazione Santa Lucia IRCCS, Via Ardeatina 306-354, Rome 00179, Italy

E-mail Address: [email protected]

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Floriana Pichiorri

Fondazione Santa Lucia IRCCS, Via Ardeatina 306-354, Rome 00179, Italy

E-mail Address: [email protected]

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, and

Jlenia Toppi

Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Rome 00185, Italy

Fondazione Santa Lucia IRCCS, Via Ardeatina 306-354, Rome 00179, Italy

E-mail Address: [email protected]

Corresponding author.

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https://doi.org/10.1142/S0129065721500520Cited by:15 (Source: Crossref)

This article is part of the issue:

Special Issue: Brain/Neural Assistive Technologies
Guest Editors: Surjo R. Soekadar, Yasuharu Koike and Loredana Zollo

Abstract

Hybrid Brain–Computer Interfaces (BCIs) for upper limb rehabilitation after stroke should enable the reinforcement of “more normal” brain and muscular activity. Here, we propose the combination of corticomuscular coherence (CMC) and intermuscular coherence (IMC) as control features for a novel hybrid BCI for rehabilitation purposes. Multiple electroencephalographic (EEG) signals and surface electromyography (EMG) from 5 muscles per side were collected in 20 healthy participants performing finger extension (Ext) and grasping (Grasp) with both dominant and non-dominant hand. Grand average of CMC and IMC patterns showed a bilateral sensorimotor area as well as multiple muscles involvement. CMC and IMC values were used as features to classify each task versus rest and Ext versus Grasp. We demonstrated that a combination of CMC and IMC features allows for classification of both movements versus rest with better performance (Area Under the receiver operating characteristic Curve, AUC) for the Ext movement (0.97) with respect to Grasp (0.88). Classification of Ext versus Grasp also showed high performances (0.99). All in all, these preliminary findings indicate that the combination of CMC and IMC could provide for a comprehensive framework for simple hand movements to eventually be employed in a hybrid BCI system for post-stroke rehabilitation.

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References

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Accepted 27 August 2021

Published: 30 September 2021

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Corticomuscular and Intermuscular Coupling in Simple Hand Movements to Enable a Hybrid Brain–Computer Interface

Abstract

References

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Corticomuscular and Intermuscular Coupling in Simple Hand Movements to Enable a Hybrid Brain–Computer Interface

Abstract

References

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