Next Article in Journal
A Path-Planning Performance Comparison of RRT*-AB with MEA* in a 2-Dimensional Environment
Previous Article in Journal
Some Novel Picture 2-Tuple Linguistic Maclaurin Symmetric Mean Operators and Their Application to Multiple Attribute Decision Making
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Symmetry
Volume 11
Issue 7
10.3390/sym11070944
symmetry-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Individual-Specific Classification of Mental Workload Levels Via an Ensemble Heterogeneous Extreme Learning Machine for EEG Modeling

by
Jiadong Tao
1,2,
Zhong Yin
1,2,*,
Lei Liu
3,
Ying Tian
1,2,
Zhanquan Sun
1,2 and
Jianhua Zhang
4
1
Engineering Research Center of Optical Instrument and System, Ministry of Education, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 200093, China
2
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
3
School of Management, University of Shanghai for Science and Technology, Shanghai 200093, China
4
OsloMet Artificial Intelligence Lab, Department of Computer Science, Oslo Metropolitan University, N-0130 Oslo, Norway
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(7), 944; https://doi.org/10.3390/sym11070944
Submission received: 21 May 2019 / Revised: 19 June 2019 / Accepted: 16 July 2019 / Published: 20 July 2019
Browse Figures
Versions Notes

Abstract

In a human–machine cooperation system, assessing the mental workload (MW) of the human operator is quite crucial to maintaining safe operation conditions. Among various MW indicators, electroencephalography (EEG) signals are particularly attractive because of their high temporal resolution and sensitivity to the occupation of working memory. However, the individual difference of the EEG feature distribution may impair the machine-learning based MW classifier. In this paper, we employed a fast-training neural network, extreme learning machine (ELM), as the basis to build an individual-specific classifier ensemble to recognize binary MW. To improve the diversity of the classification committee, heterogeneous member classifiers were adopted by fusing multiple ELMs and Bayesian models. Specifically, a deep network structure was applied in each weak model aiming at finding informative EEG feature representations. The structure of hyper-parameters of the proposed heterogeneous ensemble ELM (HE-ELM) was then identified and then its performance was compared against several competitive MW classifiers. We found that the HE-ELM model was superior for improving the individual-specific accuracy of MW assessments.
Keywords: electroencephalography; mental workload; extreme learning machine; ensemble learning electroencephalography; mental workload; extreme learning machine; ensemble learning

Share and Cite

MDPI and ACS Style

Tao, J.; Yin, Z.; Liu, L.; Tian, Y.; Sun, Z.; Zhang, J. Individual-Specific Classification of Mental Workload Levels Via an Ensemble Heterogeneous Extreme Learning Machine for EEG Modeling. Symmetry 2019, 11, 944. https://doi.org/10.3390/sym11070944

AMA Style

Tao J, Yin Z, Liu L, Tian Y, Sun Z, Zhang J. Individual-Specific Classification of Mental Workload Levels Via an Ensemble Heterogeneous Extreme Learning Machine for EEG Modeling. Symmetry. 2019; 11(7):944. https://doi.org/10.3390/sym11070944

Chicago/Turabian Style

Tao, Jiadong, Zhong Yin, Lei Liu, Ying Tian, Zhanquan Sun, and Jianhua Zhang. 2019. "Individual-Specific Classification of Mental Workload Levels Via an Ensemble Heterogeneous Extreme Learning Machine for EEG Modeling" Symmetry 11, no. 7: 944. https://doi.org/10.3390/sym11070944

APA Style

Tao, J., Yin, Z., Liu, L., Tian, Y., Sun, Z., & Zhang, J. (2019). Individual-Specific Classification of Mental Workload Levels Via an Ensemble Heterogeneous Extreme Learning Machine for EEG Modeling. Symmetry, 11(7), 944. https://doi.org/10.3390/sym11070944

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop