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

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

doi:10.1109/access.2020.3038807

Chih-Sung Chen, Ting-Kuang Yeh + Show 3 more

Open Access

https://doi.org/10.1109/access.2020.3038807

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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.

Highlights

Cognitive load is essential for understanding instructional design quality and optimizing working memory capacity on learning during instruction [1]
The ensemble empirical mode decomposition (EEMD) method is intuitive, direct, and adaptive with an a posteriori-defined basis [38, 40]. This method presumes that any data are composed of a series of simple oscillatory modes of different frequency bands called intrinsic mode functions (IMFs) which fulfill the following two conditions: (1) the number of minima, maxima, and the number of zero crossings must be equal, or differ by one at most; (2) the envelopes determined by the local maxima and that by the local minima are symmetrical to zero [40]
A detailed comparison of our method with the Fourier based technique using data taking from a representative participant is shown in Fig. 5 where Figs 5a, 5c, and 5e are the EEMD-marginal Hilbert-Huang spectrum (MHHS) derived responding Theta amplitude of individual question on each difficulty level in three positions, Fpz, Fp2, and Fp1, respectively

Summary

Introduction

Cognitive load is essential for understanding instructional design quality and optimizing working memory capacity on learning during instruction [1]. Dan and Reiner [7] indicated that observing psycho-physiological changes when they occur in response to the progression of a learning session allows for adjusting the individual learner’s capabilities. This observation usually can be achieved by detecting the brain’s electrical activities. Electroencephalograph (EEG) is a non-invasive electrophysiological monitoring device to detect electrical activities of the brain [8]. The accessibility of the EEG device and advanced near-real-time analysis techniques have improved the quality of teaching and learning in various aspects [3, 4, 7]. The EEG is an effective tool in the study of machine learning, such as providing new communication and control options for individuals to interact with the external world [9] or monitoring human driving behavior to reduce traffic accidents [10]

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