# Dynamic Ensemble Selection for EEG Signal Classification in Distributed Data Environments
## 3 Datasets and Experimental Results
A comparative box plot illustrating the F1-weighted results for the six approaches is provided in Figure 5. Based on the graph, it can be seen that worse results were achieved for the diverse groups with abstract level approach. As can be seen, the observed performance differences across the two datasets highlight the importance of dataset-specific characteristics in model evaluation. The Epilepsy dataset was carefully curated and preprocessed by its authors to facilitate model training--signals were clearly segmented, of equal length, and correctly labeled for each mental state. In contrast, the BCI dataset was explicitly designed to be challenging: it comprises continuous, unsegmented signals with unknown transitions between mental states, variable trial durations (1.5-8 s), class imbalance, and artificially inserted \"no control\" periods. These differences substantially impact model performance and suggest that care must be taken when generalizing results across datasets with differing structures and assumptions. The results presented above are compared to those obtained without applying distributed environments. Admittedly, this comparison is not entirely fair, as the non-dispersed approach allows the model access to all available knowledge simultaneously. Figure 5: Comparison of F1-weighted obtained for all analyzed approaches. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \(p\)**-Value** & \begin{tabular}{l} **Abstract** \\ **Level** \\ **Sum** \\ \end{tabular} & \begin{tabular}{l} **Measurement** \\ **Level** \\ **Sum** \\ \end{tabular} & \begin{tabular}{l} **Unified** \\ **Groups** \\ **Subract Level** \\ \end{tabular} & \begin{tabular}{l} **Diverse** \\ **Groups** \\ **Subract Level** \\ \end{tabular} & \
\begin{tabular}{l} **Unified Groups** \\ **Measurement** \\ **Level** \\ \end{tabular} &
\begin{tabular}{l} **Diverse Groups** \\ **Measurement** \\ **Level** \\ \end{tabular} \\ \hline Abstract level, sum & & 1 & 1 & 0.456 & 1 & 1 \\ Measurement level, sum & 1 & 1 & 0.132 & 1 & 1 \\ Unified groups, abstract level & 1 & 1 & 1 & 1 & 1 \\ Diverse groups, abstract level & 0.456 & 0.132 & 1 & 0.426 & 0.009 \\ Unified groups, & & 1 & 1 & 0.426 & & 1 \\ measurement level & 1 & 1 & 1 & 0.009 & 1 & \\ Diverse groups, & & & & & & \\ measurement level & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: \(p\)-values for the post hoc Dunn–Bonferroni test for appeoaches: unified groups unweighted and weighted; diverse groups unweighted and weighted. (Blue text color represent best result). Nevertheless, these experiments were conducted to determine whether dispersing knowledge significantly reduces classification quality. The results obtained for both datasets and the approaches used in the paper (Random Forest, AdaBoost, Gradient Boosting, Logistic Regression) are shown in Table 6. The procedure for optimizing the parameters mirrored the strategies employed in the approaches discussed earlier. Comparing the results from the non-dispersed version (Table 6) with the dispersed approach shows that AdaBoost is the best-performing method overall. For the EEG Epilepsy dataset, the dispersed approach actually performs slightly better, with results like 0.983 accuracy and 0.98 for other metrics. This means that spreading the data out did not hurt the results--in fact, it may have helped a bit. For the BCI Competition IV Dataset 1, the distributed version scores just a little lower, with accuracy values between 0.596 and 0.601. However, this difference is very small and not really significant. Overall, using dispersed data does not reduce the quality of the results. In the case of the EEG Epilepsy data, it even improves it. For the BCI dataset, the drop in performance is so minor that it shows dispersion does not cause any real deterioration. On the contrary, distributed approaches can work just as well, or even better, depending on the dataset. We also compared our results with state-of-the-art deep learning models commonly used in EEG classification.