# EEG-Based Attention Classification for Enhanced Learning Experience
## 3 Results
### Training the k-NN Classifier  
Integrating attention mechanisms into the k-NN classifier significantly enhanced classification accuracy. The feature extraction process effectively distinguished between high and low attention states, and the optimal EEG frequency band for real-time processing was identified, contributing to the development of more efficient neurofeedback systems. Figure 6: This flowchart illustrates how the adaptive learning system operates, showing how the system monitors EEG signals in real-time to identify attention levels. It details the actions taken when low or high attention levels are detected, such as increasing repetition and reducing difficulty for low attention or increasing difficulty for high attention. Network analysis revealed that high attention states were associated with a more integrated and efficient neural network structure. Wavelet packet decomposition facilitated a detailed breakdown of EEG signals into four frequency bands: Theta (3-7 Hz), Alpha (7-15 Hz), Beta (15-30 Hz), and Gamma (30-50 Hz), as illustrated in Figure 7. These results indicated that neural synchronization patterns across these bands varied significantly between attention states. Neural synchronization across these bands revealed clear differences between high and low attention states. Table 1 presents the mean synchronization values, standard deviations, \(p\)-values, and [PERSON]'s \(d\) values for the Theta, Alpha, Beta, and Gamma frequency bands, highlighting significant differences between high attention (HA) and low attention (LA) states. Notably, HA showed higher synchronization in the Theta band, while LA exhibited greater synchronization in the Alpha, Beta, and Gamma bands. [PERSON]'s \(d\) values indicate \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Variable** & **Condition** & **Mean** & **Std. Dev.** & \(p\)**-Value** & **[PERSON]’s \(d\)** & **Comparison** \\ \hline \multirow{2}{*}{Theta} & HA & 0.5212 & 0.2528 & \(3.28\times 10^{-8}\) & 0.031 & HA \(>\) LA \\  & LA & 0.5136 & 0.2356 & \(2.38\times 10^{-20}\) & & \\ \hline \multirow{2}{*}{Alpha} & HA & 0.4462 & 0.2551 & \(4.79\times 10^{-67}\) & \(-\)0.024 & HA \(<\) LA \\  & LA & 0.4520 & 0.2354 & \(1.03\times 10^{-21}\) & & \\ \hline \multirow{2}{*}{Beta} & HA & 0.3656 & 0.2455 & \(2.54\times 10^{-13}\) & \(-\)0.068 & HA \(<\) LA \\  & LA & 0.3819 & 0.2368 & \(1.64\times 10^{-11}\) & & \\ \hline \multirow{2}{*}{Gamma} & HA & 0.3620 & 0.2420 & \(1.41\times 10^{-74}\) & \(-\)0.098 & HA \(<\) LA \\  & LA & 0.3853 & 0.2349 & \(1.00\times 10^{-29}\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean, standard deviation (Std. Dev. ), \(p\)-value, Cohen’s \(d\), and comparison between HA and LA conditions. Figure 7: Wavelet packet decomposition of the EEG signal into Theta (3–7 Hz), Alpha (7–15 Hz), Beta (15–30 Hz), and Gamma (30–50 Hz) frequency bands  
small effect sizes across all frequency bands, with the Theta band showing the smallest effect. In contrast, the Beta and Gamma bands show slightly larger, though still small, negative effects. These findings suggest that while EEG frequency bands are influenced by attention states, the practical significance of these differences remains modest. HA networks showed increased synchronization in the Theta band, while LA networks exhibited higher synchronization in the Alpha, Beta, and Gamma bands. Figure 8 illustrates neural synchronization patterns across these frequency bands within a weighted 30 x 30 network, based on the 10-20 electrode placement system. Significant differences between HA and LA conditions are highlighted, with synchronization matrices color-coded from blue (\(H=0\)) to red (\(H=1\)), indicating strong correlations between specific channel pairs. In this study, the synchronization matrix was converted into an unweighted graph for each participant by selecting a threshold value \(T_{h}\) from a range of 0.01 to 0.6, with increments of 0.001 [24].