EEG-Based Mental Fatigue Detection Using CNN-LSTM

Mingxin Su,Yiming Wen,Weimin Li,Weidong Zhou,Fulai Peng,Rui Zhang

doi:10.1109/cme55444.2022.10063316

EEG-Based Mental Fatigue Detection Using CNN-LSTM

Mingxin Su, Yiming Wen + Show 4 more

https://doi.org/10.1109/cme55444.2022.10063316

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Publication Date: Nov 4, 2022

Citations: 1

Affiliation: Shandong Academy of Sciences, Chinese Academy of Sciences, Shandong University

#Convolutional Neural Network - Long Short-Term Memory #Convolutional Neural Network + Show 8 more

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Abstract

In this study, a multi-channel Electroencephalogram (EEG) mental fatigue detection algorithm is proposed based on the Convolutional Neural Network- Long Short-Term Memory (CNN-LSTM) network. The CNN-LSTM deep network model is used to distinguish three different mental fatigue states: awake, mild fatigue and severe fatigue. The model is validated on a self-collected dataset collected by the 2-BACK experimental paradigm. The data set contains fatigue data for a total of 10 healthy adults without adverse habits. The average recognition accuracy of the model is 97.12%. The model yields a sensitivity of 97.80% and a specificity of 99.28%. Results show that our proposed CNN-LSTM model can distinguish the three different mental fatigue states effectively.

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