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Area of Science:

  • Neuroscience
  • Ophthalmology
  • Biomedical Engineering

Background:

  • Multiple Sclerosis (MS) diagnosis and monitoring rely on sensitive electrophysiological measures.
  • Multifocal-visual-evoked-potential (mfVEP) signals are crucial for assessing visual pathway function in MS.
  • Conventional mfVEP processing may have limitations in discriminating between patient groups and reducing signal variability.

Purpose of the Study:

  • To evaluate the efficacy of empirical mode decomposition (EMD) filtering for mfVEP signals.
  • To enhance the discrimination of amplitude-based mfVEP signals between control and MS patient groups.
  • To reduce interocular latency variability in mfVEP recordings from control subjects.

Main Methods:

  • mfVEP signals were recorded from healthy controls, MS patients, and MS-risk progression groups (RIS, CIS).
  • Signals were processed using conventional bandpass filtering (XDFT) and EMD-based filtering (XEMD).
  • Amplitude and latency analyses were performed on full visual field and specific eccentric regions (ring 5).

Main Results:

  • EMD-filtered mfVEP signals (XEMD) demonstrated higher discrimination indices compared to conventional XDFT processing.
  • XEMD filtering resulted in lower interocular latency variability in control subjects.
  • Optimal discrimination and latency reduction were observed in the ring 5 (9.8-15° eccentricity) of the visual field.

Conclusions:

  • EMD filtering of mfVEP signals offers improved identification of individuals at risk for MS.
  • This advanced signal processing enhances the accuracy of latency measurements for MS diagnosis and progression studies.
  • EMD-filtered mfVEP analysis holds potential for assessing visual cortex activity in MS management.