Accuracy to detection timing for assisting repetitive facilitation exercise system using MRCP and SVM View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Satoshi Miura, Junichi Takazawa, Yo Kobayashi, Masakatsu G. Fujie

ABSTRACT

This paper presents a feasibility study of a brain-machine interface system to assist repetitive facilitation exercise. Repetitive facilitation exercise is an effective rehabilitation method for patients with hemiplegia. In repetitive facilitation exercise, a therapist stimulates the paralyzed part of the patient while motor commands run along the nerve pathway. However, successful repetitive facilitation exercise is difficult to achieve and even a skilled practitioner cannot detect when a motor command occurs in patient's brain. We proposed a brain-machine interface system for automatically detecting motor commands and stimulating the paralyzed part of a patient. To determine motor commands from patient electroencephalogram (EEG) data, we measured the movement-related cortical potential (MRCP) and constructed a support vector machine system. In this paper, we validated the prediction timing of the system at the highest accuracy by the system using EEG and MRCP. In the experiments, we measured the EEG when the participant bent their elbow when prompted to do so. We analyzed the EEG data using a cross-validation method. We found that the average accuracy was 72.9% and the highest at the prediction timing 280 ms. We conclude that 280 ms is the most suitable to predict the judgment that a patient intends to exercise or not. More... »

PAGES

12

References to SciGraph publications

  • 2008. Control of a Wheelchair by Motor Imagery in Real Time in INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING – IDEAL 2008
  • 1999-06. Least Squares Support Vector Machine Classifiers in NEURAL PROCESSING LETTERS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s40638-017-0071-5

    DOI

    http://dx.doi.org/10.1186/s40638-017-0071-5

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1092584518

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29170726


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