Source-Based EEG Neurofeedback for Sustained Motor Imagery of a Single Leg.


Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
15 Jun 2023
Historique:
received: 03 05 2023
revised: 01 06 2023
accepted: 07 06 2023
medline: 10 7 2023
pubmed: 8 7 2023
entrez: 8 7 2023
Statut: epublish

Résumé

The aim of the study was to test the feasibility of visual-neurofeedback-guided motor imagery (MI) of the dominant leg, based on source analysis with real-time sLORETA derived from 44 EEG channels. Ten able-bodied participants took part in two sessions: session 1 sustained MI without feedback and session 2 sustained MI of a single leg with neurofeedback. MI was performed in 20 s on and 20 s off intervals to mimic functional magnetic resonance imaging. Neurofeedback in the form of a cortical slice presenting the motor cortex was provided from a frequency band with the strongest activity during real movements. The sLORETA processing delay was 250 ms. Session 1 resulted in bilateral/contralateral activity in the 8-15 Hz band dominantly over the prefrontal cortex while session 2 resulted in ipsi/bilateral activity over the primary motor cortex, covering similar areas as during motor execution. Different frequency bands and spatial distributions in sessions with and without neurofeedback may reflect different motor strategies, most notably a larger proprioception in session 1 and operant conditioning in session 2. Single-leg MI might be used in the early phases of rehabilitation of stroke patients. Simpler visual feedback and motor cueing rather than sustained MI might further increase the intensity of cortical activation.

Identifiants

pubmed: 37420769
pii: s23125601
doi: 10.3390/s23125601
pmc: PMC10301779
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Engineering and Physical Sciences Research Council
ID : EP/ N509668/1 and EP/M508056/1

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Auteurs

Anna Zulauf-Czaja (A)

Biomedical Engineering Research Division, School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.

Bethel Osuagwu (B)

Biomedical Engineering Research Division, School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.

Aleksandra Vuckovic (A)

Biomedical Engineering Research Division, School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.

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Classifications MeSH