## 5 Discussion
Such methodologies have pinpointed specific frequency bands, notably Beta2 (15-22 Hz), as critical for distinguishing between mental arithmetic tasks and baseline states, achieving classification accuracies up to 89%. This underscores the intricate web of neural interactions that underpin mathematical cognition and its dependency on effective inter-regional communication [49]. Moreover, recent advancements in EEG analysis techniques, including using the generalized Higuchi fractal dimension spectrum (GHFDS), have significantly enhanced the accuracy of recognizing mental arithmetic tasks from EEG signals. Researchers have improved task recognition accuracy by combining GHFDS with other features, such as power spectrum density and statistical features, paving the way for more sophisticated brain-computer interfaces that can adapt to the user's cognitive state in real time [50]. In summary, integrating these findings into our discussion not only reinforces the validity of our results but also highlights the complexity of the neural mechanisms involved in arithmetic cognition. By drawing on diverse analytical techniques and findings from the broader literature, we can better understand the multifaceted nature of brain activity during mathematical tasks. This synthesis contributes to the academic discourse on cognitive neuroscience and suggests practical implications for educational strategies and clinical assessments, paving the way for future interdisciplinary research in this field. Applying graph theoretical measures such as global efficiency, betweenness centrality, and clustering coefficient to the EEG data analysis offers a novel perspective on the organization and functionality of brain networks during cognitive tasks. The association between higher global efficiency and clustering coefficients in good performers and enhanced cognitive processing capabilities suggests that these graph measures could be valuable indicators of cognitive efficiency [51]. The high classification accuracies achieved using the proposed BSL features across different frequency bands underscore the potential of machine learning models, specifically GCN, in distinguishing between high and low cognitive load states at an individual level. This capability is particularly promising for developing adaptive cognitive training and neurofeedback systems, where real-time cognitive load assessment can be used to adjust task difficulty or provide feedback to optimize learning and cognitive performance. The comparative analysis of different functional-connectivity features highlights the superiority of the proposed BSL features, mainly when used in conjunction with the GCN model. This comparison validates the effectiveness of the proposed method in accurately classifying mental arithmetic task-related cognitive load. It underscores the importance of selecting appropriate functional-connectivity features and classification models to capture the complex dynamics of brain connectivity during cognitive tasks. The manuscript reviews and confirms the effectiveness of the BSL and GCN methods by applying them to other ablation datasets. This validation process is crucial for assessing both the consistency and reproducibility of these methodologies across different datasets. Such a rigorous testing framework ensures that the approaches are robust and reliable when faced with varying data conditions. Limitations: This paper performs a subject-specific classification; therefore, the model needs to train and classify each subject dataset individually. The findings presented in this manuscript are specific to the computational setting used for this study. Therefore, outcome variations may occur when employing k-fold cross-validation in different computational settings. The study's findings are based on a dataset of 36 healthy subjects with a mean age of 18, which may limit the generalizability of the results to broader populations, including different age groups or subjects with neurological conditions.