## 5 Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & \
\begin{tabular}{c} **Accuracy (\%)** \\ **Best Classification** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy (\%)** \\ **Average of All Subjects** \\ \end{tabular} \\ \hline SVM & \(89\pm 0.33\) & \(72\pm 3.48\) \\ CNN & \(90\pm 0.68\) & \(86\pm 0.14\) \\ k-NN & \(62\pm 0.37\) & \(58\pm 2.55\) \\ LDA & \(72\pm 0.47\) & \(68\pm 0.08\) \\ Proposed model & \(94\pm 1.29\) & \(87.35\pm 1.27\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance comparison of the proposed BSL functional-connectivity classification with other classifiers for ablation-dataset-2. underscore the adaptability and efficiency of BSL in modeling complex brain networks with potentially fewer assumptions and constraints. By selecting these diverse methods for comparison, the study ensures a comprehensive evaluation of BSL across various dimensions of functional-connectivity analysis, including robustness to artifacts (AEC-c, ImCoh), directional connectivity (PTE), and network sparsity (graphical LASSO). These methods are well-established in the literature, providing a solid benchmark to demonstrate the advancements or unique contributions of the BSL algorithm in capturing the dynamic functional connectivity of the brain during cognitive tasks. This comparison highlights BSL's innovative approach to estimating dynamic EEG functional connectivity, showcasing its potential superiority in accuracy, reliability, and the capture of complex connectivity patterns. This structured comparison validates the effectiveness and novelty of the BSL algorithm and contributes to the broader discourse on methodologies for EEG functional-connectivity analysis, offering insights that may guide future research in the field. The behavioral data analysis provided insights into subjects' performance on the mental arithmetic task, revealing a clear distinction between good and bad performers based on the number of correct subtractions. This differentiation validates the experimental design and suggests a potential correlation between the subjects' cognitive load capacity and their arithmetic task performance. The observed variance in performance could be reflective of individual differences in working memory capacity, attentional resources, and mental efficiency, highlighting the importance of considering behavioral outcomes alongside neural data for a comprehensive understanding of cognitive task effects. The exploration of EEG brain activations during mental arithmetic tasks has unveiled significant insights into the neural substrates of cognitive processes. One notable finding is the increased activity in the left limbic lobe, particularly within the beta frequency band, in individuals who exhibit superior performance in mental arithmetic. This observation suggests the limbic lobe's involvement in critical functions such as visuospatial processing, memory retrieval for arithmetic facts, and emotional regulation during cognitive tasks [48]. This aligns with the broader understanding of the brain's adaptability and specialization in response to complex cognitive demands. Further investigations into EEG signal characteristics, such as power spectral density and coherence during mental arithmetic, have distinguished individuals based on task performance. Fourier analysis studies have categorized participants as good or poor counters, illustrating how EEG metrics can be reliable indicators of cognitive task engagement and efficiency [27]. This differentiation highlights the potential of EEG in personalizing learning and cognitive training programs by identifying individual strengths and areas for improvement. The application of connectivity analyses like directed transfer function (DTF) and generalized partial directed coherence (GPDC) provided more profound insights into the functional network dynamics of the brain during arithmetic reasoning.