## 2 Literature Review  
Electroencephalography (EEG) is recognized as a physiological index that is capable of continuously measuring cognitive load [39]. However, EEG signals exhibit characteristics such as weakness, noise, and non-stationarity among subjects, making the identification of robust features a persistent challenge [40]. Traditional methods for cognitive workload classification from EEG data encompass various analytical techniques and feature extraction methods. These include statistical tests, validating differences among features by examining changes in power within specific frequency bands, and time-domain features, such as amplitude, latency, and waveform morphology, providing insights into the temporal characteristics of EEG signals [41; 42; 43]. Additionally, power spectral density analysis reveals the distribution of power across frequency bands [44], aiding in the inference of workload variations. Event-related potentials (ERPs) reflect the brain's neural responses to stimuli or events, with the analysis of ERP amplitude and latency elucidating cognitive processing and workload demands [45]. EEG signals are also segmented into different frequency bands, including delta, theta, alpha, beta, and gamma [46], with the activity within these bands providing information about cognitive workload levels, particularly in the gamma band, known for its role in higher cognitive functions [47]. Furthermore, traditional classification methods such as support vector machines (SVM) [48], k-nearest neighbors (kNN) [49], and naive Bayes classifiers [50] have been widely employed for cognitive workload classification based on EEG data. Significant advancements have been made in understanding cognitive workload using EEG, using machine learning approaches to make valuable contributions towards its assessment. Machine learning algorithms apply features learned from a training dataset to perform tasks such as classification or regression [51]. On the other hand, deep learning is a specific subclass of machine learning that constructs models based on neural networks, such as convolutional networks or long short-term memory networks. Unlike traditional machine learning, deep learning models have the capability to learn complex patterns and representations directly from raw data, eliminating the need for manual feature extraction [52; 53]. With automated feature learning, deep learning models can achieve a high recognition accuracy by training directly on raw datasets, allowing for more efficient and accurate cognitive workload classification [53]. In exploring the landscape of cognitive workload detection, several studies have contributed significant insights leveraging machine learning approaches. [PERSON] et al. (2019) [54] utilized extreme gradient boosting (XGB) algorithms to detect cognitive workload, showing promising results in drone control simulation experiments. [PERSON] et al. (2021) [55] emphasized the feasibility of short-calibration methods for mental fatigue modeling through biometric signals. [PERSON] et al. (2021) focused on mental fatigue and human-machine interactions, while [PERSON] et al. (2021) [56] addressed job execution support using wearable EEG devices. [PERSON] et al. (2022) [57] introduced a novel framework using hybrid EEG-fNIRS and bivariate functional brain connectivity (FBC) features for workload classification. Additionally, [PERSON] et al. (2023) [58] developed a real-time system for identifying pilots' cognitive workload levels using a wireless EEG headset, contributing to EEG-based methodologies for real-time assessments in aviation. Despite achieving a lower accuracy rate compared to previous studies, [PERSON] et al. 's research provides valuable insights into cognitive workload assessments in aviation contexts. Studies using deep learning approaches include [PERSON] et al. (2021) [59], who developed a cascade one-dimensional convolution neural network (1 DCNN) and bidirectional long short-term memory (BLSTM) model for binary and ternary classification of mental workload (MWL).