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Brain Network Regional Synchrony Analysis in Deafness.

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This study uses electroencephalography (EEG) functional connectivity to differentiate children with cochlear implants (CI) from those who are prelingually deaf. The novel method achieves high accuracy, aiding in evaluating cochlear implantation effectiveness.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Auditory Science

Background:

  • Deafness is a common auditory disease with cochlear implantation (CI) as a primary treatment.
  • Objective indicators for evaluating CI effectiveness are currently lacking.
  • Distinguishing CI users from prelingually deafened children is crucial for auditory rehabilitation.

Purpose of the Study:

  • To develop an objective EEG-based method to differentiate children with cochlear implants (CI) from prelingually deafened children without CI.
  • To evaluate the effectiveness of functional connectivity analysis for classifying these groups.
  • To introduce and assess a novel region of interest (ROI) partitioning method for EEG analysis.

Main Methods:

  • Functional connectivity analysis focusing on brain network regional synchrony.
  • Computation of inter- and intra-regional synchrony between EEG channel pairs.
  • Feature vector generation using synchrony values for Support Vector Machine (SVM) classification.
  • Development and comparison of a new ROI partition method against an existing one for 128-channel EEG.

Main Results:

  • The proposed functional connectivity analysis method achieved significant classification improvements.
  • Accuracy rates of 87.20% and 86.30% were obtained using the existing and proposed ROI partition methods, respectively.
  • The new ROI partition method demonstrated comparable performance to the existing method.

Conclusions:

  • EEG-based functional connectivity analysis is a promising objective indicator for evaluating cochlear implantation effectiveness.
  • The developed method effectively distinguishes CI children from prelingually deafened children.
  • The novel ROI partition method is a viable alternative for EEG analysis in this context.