Effects of Visual-Electrotactile Stimulation Feedback on Brain Functional Connectivity during Motor Imagery Practice

Chatrin Phunruangsakao, David Achanccaray, Saugat Bhattacharyya, Shin-Ichi Izumi, Mitsuhiro Hayashibe

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
19 Downloads (Pure)

Abstract

The use of neurofeedback is an important aspect of effective motor rehabilitation as it offers real-time sensory information to promote neuroplasticity. However, there is still limited knowledge about how the brain’s functional networks reorganize in response to such feedback. To address this gap, this study investigates the reorganization of the brain network during motor imagery tasks when subject to visual stimulation or visual-electrotactile stimulation feedback. This study can provide healthcare professionals with a deeper understanding of the changes in the brain network and help develop successful treatment approaches for brain–computer interface-based motor rehabilitation applications. We examine individual edges, nodes, and the entire network, and use the minimum spanning tree algorithm to construct a brain network representation using a functional connectivity matrix. Furthermore, graph analysis is used to detect significant features in the brain network that might arise in response to the feedback. Additionally, we investigate the power distribution of brain activation patterns using power spectral analysis and evaluate the motor imagery performance based on the classification accuracy. The results showed that the visual and visual-electrotactile stimulation feedback induced subject-specific changes in brain activation patterns and network reorganization in the
band. Thus, the visual-electrotactile stimulation feedback significantly improved the integration of information flow between brain regions associated with motor-related commands and higher-level cognitive functions, while reducing cognitive workload in the sensory areas of the brain and promoting positive emotions. Despite these promising results, neither neurofeedback modality resulted in a significant improvement in classification accuracy, compared with the absence of feedback. These findings indicate that multimodal neurofeedback can modulate imagery-mediated rehabilitation by enhancing motor-cognitive communication and reducing cognitive effort. In future interventions, incorporating this technique to ease cognitive demands for participants could be crucial for maintaining their motivation to engage in rehabilitation.
Original languageEnglish
Article number17752 (2023)
Pages (from-to)1-15
Number of pages15
JournalScientific Reports
Volume13
Issue number1
Early online date18 Oct 2023
DOIs
Publication statusPublished online - 18 Oct 2023

Bibliographical note

Funding Information:
This work was supported by the JSPS Grant-in-Aid for Scientific Research on Innovative Areas Hyper-Adaptability Project under Grant No. 22H04764.

Publisher Copyright:
© 2023, Springer Nature Limited.

Keywords

  • Biomedical engineering
  • Brain-machine interface
  • Computational neuroscience

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