## 3 Results
### Analysis of Weighted Results  
Following the above feature dimensionality reduction analysis, the FB(CSP + CCA) combination was shown to produce more discriminative low-dimensional features. To further evaluate the classification performance of the FB(CSP + CCA) features, both XGBoost and SVM classifiers were applied. The results are illustrated in Figure 6.  
As shown in Figure 5(a),b, the FB(CSP + CCA) features achieved the highest classification accuracy under both XGBoost and SVM classifiers when compared with other individual feature sets, which is consistent with the findings from the previous dimensionality reduction analysis. Furthermore, it can be observed that the classification performance of CCA and CSP features varies slightly depending on the classifier used. To further explore this effect, classification results for the same feature types under different classifiers were plotted in the same figure, as shown in Figure 7.  
Figure 5: FB(CSP + CCA) downscaled feature map. Figure 6: Classification accuracy of different classifiers. In Figure 6(a), the classification accuracy of FBCSP-SVM can be seen to be higher than that of FBCSP-XGBoost, and CSP-SVM outperforms CSP-XGBoost. Conversely, in Figure 6(b), FBCCA-XGBoost and CCA-XGBoost achieve higher accuracies than their respective SVM counterparts. These results suggest that for features extracted using CSPs and FBCSPs, the SVM provides a better classification performance, while for features extracted using CCA and FBCCA, XGBoost is more effective. From a theoretical standpoint, an SVM utilizes kernel functions to map data into a higher-dimensional space, where it becomes linearly separable. CCA features, projected based on different stimulus frequencies, tend to be sparse in the original feature space. When these sparse features are mapped into a high-dimensional space, the increased sparsity may hinder the performance of the SVM in terms of learning and generalization. In contrast, CSP features are designed to maximize inter-class differences, making them well suited for binary classification. The distribution of CSP features is more compact in the feature space, thereby enhancing the effectiveness of SVM classification. On the other hand, XGBoost handles high-dimensional data more efficiently and captures complex nonlinear relationships through its tree-based structure, making it well suited for multi-frequency CCA-based feature vectors. Considering the complementary strengths of the SVM and XGBoost for different types of features, a weighted ensemble classifier combining the outputs of both models was proposed. The combined output was calculated using a weighting coefficient, where the sum of the weights assigned to XGBoost and SVM equals 1. When the XGBoost weight was set to 1, the SVM weight was 0, and vice versa. The average classification performance for different XGBoost weights is shown in Figure 8.  
Figure 8: Effect of weighted values on output accuracy. Figure 7: Classification results of the same type of features under different classifiers. As shown in Figure 8, the highest classification accuracy was achieved when the weight assigned to the XGBoost classifier was 0.45 and the weight assigned to the SVM classifier was 0.55. Based on this result, the ensemble model was configured with XGBoost and SVM weights set to 0.45 and 0.55, respectively. The combined features were then classified using the SVM, XGBoost, and the weighted ensemble classifiers. The classification results are presented in Figure 9, and the average performance was statistically evaluated using a paired-sample \(t\)-test. As shown in Figure 9, due to individual variability, the classification accuracy for participant S2 did not improve when using the SVM + XGBoost ensemble classifier; in fact, it was slightly lower than that achieved with the standalone SVM classifier. For all other participants, the ensemble classifier yielded a noticeable improvement.