Evaluation of Mental Workload in Working Memory Tasks with Different Information Types Based on EEG.
10.3969/j.issn.1671-7104.2022.01.003
- Author:
Kai GUAN
1
;
Sheng WANG
2
;
Zhimin ZHANG
1
;
Tao LIU
1
;
Haijun NIU
1
Author Information
1. School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083.
2. Shanghai Aviation Electric Co. Ltd., Shanghai, 201101.
- Publication Type:Journal Article
- Keywords:
EEG;
information type;
linear features;
mental workload;
nonlinear features
- MeSH:
Electroencephalography;
Memory, Short-Term;
Workload
- From:Chinese Journal of Medical Instrumentation
2022;46(1):10-15
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:Exploring the effectiveness of using EEG linear and nonlinear features for accessing mental workload in different tasks.
METHODS:Working memory tasks with different information types and various mental loads were designed based on N-Back paradigm. EEG signals from 18 normal adults were acquired when tasks were being performed. Linear and nonlinear features of EEGs were then extracted. Indices that can effectively reflect mental workload levels were selected by using multivariate analysis of variance statistical approach.
RESULTS:With the increment of task load, power of frontal Theta, Theta/Alpha ratio, and sample entropies (scales>10) in parietal regions increased significantly first and decreased slightly then, while the power of central-parietal Alpha decreased significantly first and increased slightly then. No difference in power of frontal Theta, central-parietal Alpha, and sample entropies (scales>10) of parietal regions were found between verbal and object tasks, as well as between two spatial tasks. No difference of frontal Theta/Alpha ratio was found in all the four tasks.
CONCLUSIONS:The results can provide evidence for the mental workload evaluation in tasks with different information types.
