A novel method of mental fatigue detection based on CNN and LSTM
by Shaohan Zhang; Zhenchang Zhang; Zelong Chen; Shaowei Lin; Ziyan Xie
International Journal of Computational Science and Engineering (IJCSE), Vol. 24, No. 3, 2021

Abstract: Mental fatigue is a state that may occur due to excessive work or long-term stress. Electroencephalography (EEG) is considered a reliable standard for mental fatigue detection. The existing EEG fatigue detection methods mainly use traditional machine learning models to classify mental fatigue after manual feature extraction. However, manual feature extraction is difficult and complicated. The quality of feature extraction largely determines the quality of the model. In this article, we collected EEG signals from 30 medical staff. The wavelet threshold denoising method was then applied to the measured EEG signal data to denoise the original EEG data, and a method based on a convolution and long short-term memory (CNN + LSTM) neural network to determine the fatigue state of medical staff. The extensive experiment on the established dataset clearly proves the advancement of our proposed algorithm compared to other neural network-based methods. Compared with the existing DNN, CNN and LSTM, the proposed model can quickly learn the information before and after the time series, so as to obtain higher classification accuracy.

Online publication date: Tue, 15-Jun-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Science and Engineering (IJCSE):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com