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

被引:4
作者
Chen, Chih-Sung [1 ,3 ]
Chien, Tsungh-Sin [1 ,2 ]
Lee, Po-Lei [3 ]
Jeng, Yih [1 ]
Yeh, Ting-Kuang [1 ,2 ]
机构
[1] Natl Taiwan Normal Univ, Dept Earth Sci, Taipei 106, Taiwan
[2] Natl Taiwan Normal Univ, Inst Marine Environm Sci & Technol, Taipei 106, Taiwan
[3] Natl Cent Univ, Dept Elect Engn, Taoyuan 32001, Taiwan
来源
IEEE ACCESS | 2020年 / 8卷 / 08期
关键词
Electroencephalograph; cognitive load; ensemble empirical mode decomposition; multi-marginal Hilbert-Huang spectrum; brain rhythms; EMPIRICAL MODE DECOMPOSITION; WORKING-MEMORY; QUANTITATIVE EEG; ALPHA; THETA; TRANSFORM; CLASSIFICATION; OSCILLATIONS; PERFORMANCE;
D O I
10.1109/ACCESS.2020.3038807
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:211115 / 211124
页数:10
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