## 4 Results and Analysis
### The Results Obtained from the DGNN Model  
The DGNN model integrates bidirectional long short-term memory (Bi-LSTM) and gated recurrent unit (GRU) architectures to analyze EEG data efficiently. The Bi-LSTM and GRU components within the DGNN framework enable comprehensive analysis of temporal dependencies and intricate patterns within EEG signals. Bi-LSTM excels in capturing long-term dependencies, while GRU enhances the model's ability to learn from  
Figure 8: Multi-layer cognitive workload monitoring framework. sequential data efficiently. Our findings showcase a remarkable achievement in cognitive workload classification. The DGNN model demonstrates an impressive accuracy rate of 99.45%, as depicted in Figure 9a. The accuracy curve, spanning 50 training epochs, reveals a consistent improvement. In contrast, Figure 9b illustrates the curve of loss values, depicting a continual decrease as the model iteratively learns from the training data. These figures provide a clear visualization of the model's performance dynamics, underscoring its robustness and efficacy in cognitive workload classification. This suggests promising applications in practical industrial settings. It can enhance safety protocols, optimize task allocation, and improve overall productivity and worker well-being. The confusion matrix serves as a crucial tool for evaluating the accuracy and reliability of cognitive workload level predictions. In Figure 10, the confusion matrix of our DGNN model showcases significant achievements in workload classification. Specifically, the model accurately classified 93,807 instances as '0' for 'low workload levels'. Furthermore, it correctly identified 98,229 instances as '1' for'medium workload levels', demonstrating its effectiveness in recognizing moderate cognitive workload levels. Moreover, the model achieved precise classifications in 172,444 instances as '2' for 'high workload levels', indicating its proficiency in accurately identifying instances of high cognitive workloads. These findings underscore the importance of the confusion matrix in revealing how effectively the DGNN model handles different workload levels. By quantifying its classification outcomes, we gain valuable insights into the strengths and areas for improvement of the model, ultimately contributing to the enhancement of cognitive workload assessment techniques. The classification metrics, as presented in Table 3, provide a detailed evaluation of the model's ability to accurately classify cognitive workload levels based on the STEW EEG dataset. These metrics includes precision, recall, and F1 score values for each workload category, including low, average, and high. With precision scores of 1.00 for low workloads and 0.98 for the average and high workload categories, the model demonstrates a high accuracy in identifying instances belonging to specific workload levels. Moreover, recall scores of 1.00 for low workloads and 0.98 for the average and high workload categories indicate the model's ability to capture a high proportion of the actual instances within each workload level. The F1 score values further confirm the model's balanced performance  
Figure 9: (**a,b**) Deep gated neural network model accuracy and loss. across all workload categories, with values of 1.00 for low workloads and 0.98 for the average and high workload categories. In our study, we conducted a comparative analysis of our proposed DGNN model with existing studies in cognitive workload classification, encompassing various methodologies including machine learning and deep learning approaches. This analysis involved evaluating the methodologies employed in these studies and comparing their achieved accuracy rates. The existing methodologies in these studies included techniques such as XGBoost (XGB), multiple linear regression (MLR), random forests (RFs), support vector machines (SVMs), k-nearest neighbors (KNN), 1D convolutional neural networks (1D CNNs), deep neural networks (DNNs), and bidirectional gated networks (BDGNs).