Synchronous analysis of brain regions based on multi-scale permutation transfer entropy

Yunyuan Gao, Huixu Su, Rihui Li, Yingchun Zhang

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

The coupling of electroencephalographic (EEG)signals reflects the interaction between brain regions, which is of great importance for the assessment of motor function in post-stroke patients. In this study, the measurement of multi-scale permutation transfer entropy (MPTE)was presented and employed to characterize the coupling between the EEG signals measured from the bilateral motor and sensory areas. Post-stroke patients (n = 5)and healthy volunteers (n = 6)were recruited and participated in a hand grip task with different levels of contraction. MPTE values were computed and analyzed across various frequency bands for all subjects. Results showed that, for healthy controls, the coupling between motor and sensory areas was bi-directional and tended to be strongest in beta band. In particular, greater beta-band MPTE was found in the dominant hand and coupling strength decreased as contraction strength increased. Additionally, coupling between the motor and sensory areas of stroke patients exhibited weaker beta-band MPTE than that of healthy controls. Findings suggest that MPTE is able to quantitatively characterize the coupling properties between multiple brain regions, providing a promising approach to study the underlying mechanisms of functional motor recovery.

Original languageEnglish (US)
Pages (from-to)272-279
Number of pages8
JournalComputers in Biology and Medicine
Volume109
DOIs
StatePublished - Jun 2019

Keywords

  • Electroencephalography
  • Multi-scale permutation transfer entropy
  • Synchronous relationship

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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