## 4 Results
### Comparison with State-of-the-Art Methods  
Table 4 compares various machine learning approaches for classifying EEG data for mental arithmetic tasks, highlighting the diversity in feature extraction techniques, classifiers, and the achieved accuracies. Ref. [42] utilizes mean segmented samples and standard deviation as features and applies an SVM (support vector machine) classifier, achieving an accuracy of 92.5%. Ref. [43] learn directly from raw EEG data using ANNs (artificial neural networks) and LSTM (long short-term memory) networks, with 96.8% and 94% accuracy, respectively. This approach emphasizes the effectiveness of deep learning models in handling raw EEG data. Ref. [1] explores time-domain and frequency-domain features and uses a Gaussian Naive Bayes classifier, resulting in an 85% accuracy. This  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & \
\begin{tabular}{c} **Accuracy (\%)** \\ **(Best Classification)** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy (\%)** \\ **Average of All Subjects** \\ \end{tabular} \\ \hline AEC-c & 89 \(\pm\) 0.84 & 85 \(\pm\) 0.67 \\ ImCoh & 90 \(\pm\) 0.28 & 82 \(\pm\) 0.32 \\ PTE & 76 \(\pm\) 0.25 & 65 \(\pm\) 0.16 \\ Graphical LASSO & 86 \(\pm\) 1.26 & 79 \(\pm\) 0.45 \\ BSL (Theta) & 97 \(\pm\) 0.89 & 87 \(\pm\) 1.78 \\ BSL (Alpha) & 86 \(\pm\) 1.29 & 79 \(\pm\) 2.13 \\ BSL (Beta) & 98 \(\pm\) 1.98 & 91 \(\pm\) 2.26 \\ BSL (Gamma) & 89 \(\pm\) 1.13 & 80 \(\pm\) 2.19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of classification of BSL functional connectivity with state-of-the-art functional-connectivity features using GCN classifier.  
\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 & 85 & 72 \\ CNN & 76.9 & 68 \\ k-NN & 59 & 53 \\ LDA & 57 & 42 \\ proposed model & 98 \(\pm\) 1.98 & 91 \(\pm\) 2.26 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of classification of BSL features using GCN and other classifiers.  
indicates a focus on both temporal and spectral characteristics of EEG signals. Ref. [44] works with frequency domain features, spectral entropy, and Shannon entropy, employing neural networks for classification, which yields an 88.8% accuracy. These entropy-based features indicate the complexity and information content in EEG signals. Ref. [45] adopts neighborhood component analysis as their feature extraction technique and uses KNNs (K-nearest neighbors) for classification, obtaining a 77.3% accuracy. This approach focuses on reducing dimensionality while preserving the structure necessary for classification. Ref. [46] utilizes multivariate multiscale entropy as features and an SVM classifier, achieving a 90% accuracy. This method assesses the complexity of EEG signals at multiple scales, highlighting the signal's dynamic behavior. Ref. [47] focuses on PSD (power spectral density) features and uses EEG-TopoNet for classification, with an accuracy of 94.2%. This innovative approach likely leverages topological data analysis or network-based features derived from PSD. The proposed method utilizes BSL features and a GCN classifier, reporting the highest classification accuracy among all subjects at 98% and an average accuracy of 91%. Therefore, the results suggest that the proposed method outperforms the other listed techniques in terms of both individual and average classification accuracies.