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One-Dimensional Local Binary Pattern and Common Spatial Pattern Feature Fusion Brain Network for Central Neuropathic

Fangzhou Xu1,2, Chongfeng Wang1, Xin Yu1

  • 1International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China.

International Journal of Neural Systems
|May 15, 2023
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Summary

Spinal cord injury (SCI) alters brain connectivity, with central neuropathic pain (CNP) further increasing frontal lobe connectivity. This research identifies EEG biomarkers for CNP and achieves high accuracy in classifying motor imagery for brain-computer interfaces.

Keywords:
Spinal cord injurycentral nervous painfunctional connectivitymotor imageryphase locking value

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Clinical Neurology

Background:

  • Central neuropathic pain (CNP) following spinal cord injury (SCI) is linked to cerebral cortex plasticity.
  • Electroencephalogram (EEG) signals can capture cortical plasticity, serving as a potential biomarker for CNP.
  • Understanding brain network alterations in SCI patients with and without CNP is crucial.

Purpose of the Study:

  • To investigate changes in brain network functional connectivity in SCI patients with and without CNP.
  • To identify EEG-based biomarkers for CNP.
  • To develop and validate a brain-computer interface (BCI) system for CNP patients.

Main Methods:

  • Recorded EEG data from SCI patients with CNP, SCI patients without CNP, and healthy controls.
  • Utilized phase-locking value to construct brain network topological connectivity maps.
  • Extracted 1D-Local Binary Pattern (1D-LBP) and Common Spatial Pattern (CSP) features from motor imagery (MI) data for classification.

Main Results:

  • Spinal cord injury (SCI) increased functional connectivity between frontal and other brain regions in the alpha and beta frequency bands.
  • Central neuropathic pain (CNP) significantly enhanced functional connectivity within the frontal lobes of SCI patients.
  • The LBP-CSP feature method achieved a highest accuracy of 98.6% and an average accuracy of 91.5% in classifying left and right hand movements from MI data.

Conclusions:

  • EEG-based analysis of brain network connectivity can serve as a biomarker for CNP after SCI.
  • The LBP-CSP method shows high potential for developing effective BCIs for CNP patients.
  • Findings have significant clinical implications for neural rehabilitation and BCI applications in CNP management.