## 5 Discussion  
This study presented a novel framework for classifying mental-arithmetic-task load using BSL features and the GCN classification model. Our approach demonstrated superior performance in distinguishing between rest vs. mental arithmetic tasks, as evidenced by the high classification accuracies across different EEG frequency bands. These findings underscore the potential of dynamic EEG functional-connectivity analysis, combined with advanced machine learning models, for understanding cognitive load and brain network dynamics during mental arithmetic tasks. The statistical analysis, revealing significant differences in connectivity features across various frequency bands (theta, alpha, beta, and gamma), highlights the complexity of brain function during mental arithmetic tasks. Notably, the accuracy in the theta and beta bands suggests that specific frequencies are more indicative of cognitive load changes--a finding that could inform targeted interventions for cognitive enhancement. Furthermore, the comparison with state-of-the-art methods reveals the superiority of the proposed GCN model over traditional machine learning algorithms. This underscores the potential of advanced neural network architectures in handling EEG spatial and temporal complexities, providing a more accurate representation of brain activity. The reproducibility assessment conducted through Spearman correlation demonstrated the high consistency of the proposed BSL features across trials, suggesting robust feature stability. Compared to other state-of-the-art functional-connectivity features, this high reproducibility across different frequency bands indicates the reliability of BSL features in capturing the dynamic changes in brain connectivity. The consistent results in all four frequency bands reinforce the effectiveness of the proposed BSL features for analyzing EEG data in cognitive tasks, offering a promising tool for brain connectivity studies. The study compares traditional functional-connectivity methods AEC-c, ImCoh, PTE, and graphical LASSO with the proposed BSL algorithm. AEC-c is widely used in functional-connectivity analysis to measure the synchronization between the amplitude envelopes of brain signals across different regions. The correction for spatial leakage makes AEC-c a robust measure against false connectivity patterns caused by volume conduction or signal mixing [24]. Comparing BSL with AEC-c allows us to demonstrate the ability of BSL to capture true functional connectivity without being affected by such artifacts. ImCoh is selected for its effectiveness in mitigating the influence of volume conduction by focusing on the imaginary part of the coherence spectrum. This method provides insights into true oscillatory interactions between brain regions, free from the confounding effects of signal mixing [25]. The comparison with ImCoh highlights BSL's competence in identifying genuine neural interactions, potentially with greater specificity or sensitivity. PTE quantifies the directional flow of information between brain regions, offering insights into the effective connectivity that underpins cognitive processes [13]. Its inclusion in the comparative analysis emphasizes BSL's capacity to capture dynamic changes in brain connectivity patterns, including directional interactions, which are crucial for understanding cognitive task performance. Graphical LASSO is a regularization technique that infers sparse graphical models, making it suitable for elucidating the underlying network structure from high-dimensional data such as EEG [14]. By comparing BSL with graphical LASSO, you