## 4 Results
### Ablation Study with Other Datasets  
#### 4.10.1 Ablation Dataset-1  
**Reproducibility of functional connectivity**: In this investigation, a one-way ANOVA was conducted to evaluate three cognitive tasks: subtraction, music, and memory, with a significance threshold set at a \(p\)-value of \(<\)0.05. The analysis showed significant variations in the beta and theta frequency bands' powers among subjects performing different tasks, with \(p\)-values of 0.015 and 0.045, respectively. Across different participants, both beta and theta bands showed notable differences, with \(p\)-values of 0.0089 and \(<\)0.005, respectively. The alpha band also exhibited significant differences between participants (\(p<0.001\)) and was marginally significant within subjects (\(p=0.069\)). The delta band had a \(p\)-value close to the threshold at approximately 0.054, while the gamma band was statistically significant, recording a \(p\)-value of 0.0012. Therefore, all frequency bands were included in the analysis of this dataset. The reproducibility analysis (Spearman correlation) revealed that the BSL connectivity metrics exhibited high reliability, particularly in the alpha band, where a \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Features** & **Classifier** & **Accuracy (\%)** \\ \hline [PERSON] et al. [42] & Mean segmented samples and std. deviation & SVM & 92.5 \\ \hline [PERSON] et al. [43] & Learn directly from raw EEG data & ANN & 96.80 \\ \hline [PERSON] et al. [1] & Time domain and frequency domain & Gaussian Naïve Bayes classifier & 85 \\ \hline [PERSON] et al. [44] & Frequency domain, spectral entropy, Shannon entropy & Neural networks & 88.8 \\ \hline [PERSON] et al. [45] & Neighborhood component analysis & KNN & 77.3 \\ \hline [PERSON] et al. [46] & Multivariate multiscale entropy & SVM & 90 \\ \hline [PERSON] et al. [47] & PSD & EEG-TopoNet & 94.2 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline  & & 98 (highest classification accuracy among all subjects) \\ \(\mathrm{\SIUnitSymbolFace{91}}\) & \(\mathrm{\SIUnitSymbolFace{91}}\) & \(\mathrm{\SIUnitSymbolFace{91}}\) (average of all classification accuracies) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the proposed method with state-of-the-art methods in classifying mental arithmetic tasks. correlation of 0.95 was observed (see Figure 14). This was followed by similarly robust results in the beta band, with a correlation of 0.93, and the theta band, with a correlation of 0.87. **Intrasubject classification:** Table 5 provides a detailed summary of classification outcomes across various EEG frequency bands--alpha, beta, gamma, delta, and theta--for the top-performing classifications in each band, as evaluated by accuracy, sensitivity, specificity, and the Kappa coefficient. In the alpha band, subjects 19 and 49 achieved the highest classification accuracy, each scoring 0.97. In the beta band, subject 06 stood out with an impressive accuracy of 0.93 and a sensitivity rate of 0.9. The gamma band saw subject 15 reaching the top classification accuracy of 0.91. For the delta band, subjects 34 and 55 both recorded the highest accuracy at 0.86. Lastly, in the theta band, subjects 14 and 19 demonstrated exceptional  
**Comparison with traditional functional-connectivity features:** Table 6 evaluates the model's performance using various functional-connectivity features, illustrating the classification accuracy of each method across different frequency bands. The graphical LASSO, PTE, and BSL methods achieved individual classification accuracies exceeding 90% in the theta, alpha, beta, and gamma bands. In contrast, AEC-c and ImCoh recorded slightly lower accuracies, at 86% and 89%, respectively. Furthermore, when considering the average classification accuracies for all subjects, graphical LASSO and BSL demonstrated outstanding performance, surpassing 90% accuracy in both theta and alpha frequency bands.