EEG-TNet: An End-To-End Brain Computer Interface Framework for Mental Workload Estimation

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.