## 4 Results
### Ablation Study with Other Datasets
#### 4.10.2 Ablation Dataset-2  
**Reproducibility of functional connectivity:** Figure 15 highlights the correlation coefficients across EEG frequency bands, showing the alpha band leading with a correlation of 0.95, followed by theta at 0.92, gamma at 0.87, beta at 0.79, and delta at 0.73. These correlations were assessed against established functional-connectivity metrics: AEC-c, GLASSO, PTE, and ImCoh. The BSL features maintained consistent performance across all bands compared to these traditional metrics. The alpha and theta bands exhibited reliable reproducibility across all tested functional-connectivity metrics. **Performance of the proposed method with selected channels:** This section examines the influence of EEG channel selection on classification accuracy as identified in studies utilizing the relieff algorithm [40]. Different channel configurations analyzed include:  
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* Nineteen channels: Fp1, Fp2, F7, F3, Fpz, F4, F8, C3, Cz, C4, P3, Pz, P4, O1, O2, T3, T4, T5, T6. * Twenty-one channels: Adds Fpz and Oz to the nineteen-channel setup. * Thirty-two channels: Includes additional midline and parietal channels such as F1, F2, F5, F6, FCz, C5, C6, CP1, CP2, CP5, CP6, P1, P2, TP7, TP8, POz, O11h, O12h. Table 8 presents the classification accuracy of a specific method applied to EEG data across five frequency bands: delta, theta, alpha, beta, and gamma. The evaluation spans four EEG configurations with different channel counts: 19, 21, 32, and 256 channels. Results show that the 19-channel setup achieves the highest accuracy in all frequency bands except for delta, where the 21-channel setup excels slightly. However, as the number of channels increases to 32, there is a noticeable decline in accuracy across all bands, most markedly in the beta band. Expanding to 256 channels leads to a substantial decrease in accuracy in all bands, indicating potential issues with scaling or increased noise and complexity from the larger array of channels. Notably, the alpha and theta bands demonstrate the highest accuracies with the 19 and 21-channel setups, suggesting these bands are more stable and reliable for the method used, particularly in configurations with fewer channels. Given these observations, further analyses in this study will focus on the 19-channel EEG setup and continue examining the theta, alpha, and gamma bands due to their superior performance, while the delta and beta bands, which showed poorer results, will be less emphasized. Figure 15: Spearman correlation of functional-connectivity metrics in alpha, beta, theta, gamma, and delta frequency bands for ablation-dataset-2. **Male vs. female performance analysis:** The dataset comprises EEG data from twenty participants, split evenly between ten males and ten females. Female participants are between 21 and 40 years old, while male participants range from 20 to 38. Subject 3, aged 40, and subject 13, aged 38, had already obtained university degrees at the time of data collection, whereas the remainder were university students. Subject 17 recorded the highest accuracy in the theta band at 91%, as shown in Figure 16. The lowest theta band accuracy was observed for subject 12 at 81%. In the alpha band, subject 09 achieved the highest classification accuracy, approximately 94%, while subject 12 again showed the lowest accuracy. For the gamma band, subject 06 had the lowest classification accuracy at 80%, while subject 10 exhibited the highest at 90%. The theta band was particularly notable for its high sensitivity and specificity in classifications, as highlighted in Figure 17. The ROC curves, detailed in Figure 18, demonstrate the model's consistent performance across the theta, alpha, and gamma bands. **Comparison with other functional-connectivity features:** Table 9 shows that the AEC-c and proposed BSL features achieve over 85% accuracy, followed by graphical LASSO, which has an accuracy of 82%. ImCoh has the lowest classification accuracy of 67%.