Prefrontal Brain Electrical Activity and Cognitive Load Analysis Using a Non-linear and Non-Stationary Approach

Chih Sung Chen, Tsungh Sin Chien, Po Lei Lee, Yih Jeng*, Ting Kuang Yeh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This study presents an instantaneous spectrum analysis for electroencephalograph data processing that would facilitate the practice of learning and instruction through real-time measurements of the learner's cognitive load. The instantaneous spectrum analysis is derived from the ensemble empirical mode decomposition which decomposes signals into a gathering of intrinsic mode functions without mode mixing. The multi-marginal Hilbert-Huang spectrum is introduced to estimate frequency contents. As a result, the amplitude of brain rhythms related to the cognitive load can be determined accurately. A model study was performed at first to test the efficacy of the proposed algorithm by comparing with the Fourier based technique, then a prefrontal experiment was conducted to show the advantages of the proposed method. With the higher resolution and more realistic of the proposed method relative to conventional spectrum analysis, more significant features of the signal can be extracted. We believe that the proposed method has the potential to be a substantial technique in electroencephalograph data analysis.

Original languageEnglish
Article number9261434
Pages (from-to)211115-211124
Number of pages10
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020

Keywords

  • brain rhythms
  • cognitive load
  • Electroencephalograph
  • ensemble empirical mode decomposition
  • multi-marginal Hilbert-Huang spectrum

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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