Recognition of Cognitive Task Load levels using single channel EEG and Stacked Denoising Autoencoder

Evaluation of operator Cognitive Task Load (CTL) level is quite crucial in Human-Machine (HM) collaborative task environment since operator mental overload or inattention caused by abnormal CTL states may lead to human performance degradation or even catastrophic accidents. One of the most practical...

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Bibliographic Details
Published in:Chinese Control Conference pp. 3907 - 3912
Main Authors: Yin, Zhong, Zhang, Jianhua
Format: Conference Proceeding Journal Article
Language:English
Published: TCCT 01.07.2016
Subjects:
5G mobile communication
Channels
Cognitive task load
Cognitive tasks
Decision support systems
deep learning
electroencephalogram
Electroencephalography
mental workload
Noise reduction
operator functional state
Operators
Recognition
Searching
Tasks
ISSN:1934-1768
Online Access:Get full text
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Summary:Evaluation of operator Cognitive Task Load (CTL) level is quite crucial in Human-Machine (HM) collaborative task environment since operator mental overload or inattention caused by abnormal CTL states may lead to human performance degradation or even catastrophic accidents. One of the most practical approaches tackling this issue is to use ongoing electroencephalogram (EEG) in which human cognitive state can be objectively estimated. However, the accurate recognition of CTL via single channel EEG with the lowest-intrusivity to task condition is particularly challenging as EEG is characterized by individual dependency and nonstationarity. In this paper, a deep learning model designed by Stacked Denoising AutoEncoder (SDAE) is employed on single EEG channel signal to estimate binary levels (low vs. high) of CTL. By adopting a simulated HM process control system, the operator EEG data for 8 healthy subjects under different task demands were collected on two experimental sessions across two consecutive days. Based on the computed full power spectral of EEG. the number of nodes in SDAE is determined by greedy search according to the optimal training error of each layer. The shallow layers of the designed deep network are used to extract the subject-specific information related to CTL variation while the stable power features were reconstructed in those deep layers. Finally, the proposed method is demonstrated to be effective and 74% classification rate across sessions in average of all subjects were achieved.
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ISSN:1934-1768
DOI:10.1109/ChiCC.2016.7553961