EEG-TNet : An end-to-end brain computer interface framework for mental workload estimation

Chaojie FAN, Jin HU, Shufang HUANG, Yong PENG, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

9 Citations (Scopus)


The mental workload (MWL) of different occupational groups' workers is the main and direct factor of unsafe behavior, which may cause serious accidents. One of the new and useful technologies to estimate MWL is the Brain computer interface (BCI) based on EEG signals, which is regarded as the gold standard of cognitive status. However, estimation systems involving handcrafted EEG features are time-consuming and unsuitable to apply in real-time. The purpose of this study was to propose an end-to-end BCI framework for MWL estimation. First, a new automated data preprocessing method was proposed to remove the artifact without human interference. Then a new neural network structure named EEG-TNet was designed to extract both the temporal and frequency information from the original EEG. Furthermore, two types of experiments and ablation studies were performed to prove the effectiveness of this model. In the subject-dependent experiment, the estimation accuracy of dual-task estimation (No task vs. TASK) and triple-task estimation (Lo vs. Mi vs. Hi) reached 99.82 and 99.21%, respectively. In contrast, the accuracy of different tasks reached 82.78 and 66.83% in subject-independent experiments. Additionally, the ablation studies proved that preprocessing method and network structure had significant contributions to estimation MWL. The proposed method is convenient without any human intervention and outperforms other related studies, which becomes an effective way to reduce human factor risks.
Original languageEnglish
Article number869522
JournalFrontiers in Neuroscience
Publication statusPublished - 25 Apr 2022
Externally publishedYes

Bibliographical note

Funding Information:
This study was supported by the National Natural Science Foundation of China [Grant Numbers: 52075553]; the Natural Science Foundation of Hunan [Grant Numbers: 2020JJ7030]; the Hunan Science Foundation for Distinguished Young Scholars of China [Grant Numbers: 2021JJ10059]; the Postgraduate Scientific Research Innovation Project of Hunan Province [Grant Numbers: CX20210099].

Publisher Copyright:
Copyright © 2022 Fan, Hu, Huang, Peng and Kwong.


  • brain computer interface
  • deep neural network
  • ergonomics
  • mental workload
  • occupational safety


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