EEG signal analysis for human workload classification

Author

Ling, C. ; Goins, H. ; Ntuen, A. ; Li, R.

Author_Institution

Dept. of Ind. Eng., North Carolina A&T State Univ., Greensboro, NC, USA

fYear

2001

fDate

2001

Firstpage

123

Lastpage

130

Abstract

This paper provides the results of determining the state of a human pilot operator by using electroencephalograph (EEG) data. The state of a human operator is used to represent the mental (cognitive) workload experienced during task execution. This study used EEG data gathered from a crew-simulation laboratory environment. By using EEG data from twelve subjects encountering six simulated pilot workload levels, we set up a neural network to obtain an overall mean classification accuracy of over 80%. A comparison between the conventional backpropagation method and the resilient backpropagation method also shows that a significant reduction in training time can be achieved

Keywords

electroencephalography; medical signal processing; neural nets; psychology; signal classification; training; EEG signal analysis; backpropagation; classification accuracy; cognitive workload; conventional backpropagation method; crew-simulation laboratory environment; electroencephalograph data; human pilot operator; human workload classification; mental workload; neural network; resilient backpropagation method; simulated pilot workload levels; task execution; training time; Aircraft; Backpropagation; Biological neural networks; Biomedical measurements; Brain modeling; Data analysis; Electroencephalography; Frequency; Humans; Signal analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

SoutheastCon 2001. Proceedings. IEEE

Conference_Location

Clemson, SC

Print_ISBN

0-7803-6748-0

Type

conf

DOI

10.1109/SECON.2001.923101

Filename

923101