# EEG-Based Attention Classification for Enhanced Learning Experience
## 3 Results
### Training the k-NN Classifier
The average ratio between \(C_{\text{real}}\) and \(L_{\text{real}}\) was calculated as a function of \(T_{h}\) defined in Figure 9. Statistical analysis indicated that the most significant differences between high and low attention states were observed at the following thresholds: 0.42 (\(p=0.001\)) for Theta, 0.31 (\(p=0.002\)) for Alpha, 0.24 (\(p=0.005\)) for Beta, and 0.28 (\(p=0.005\)) for Gamma. Specifically, the ratio of \(C_{\text{real}}\) Theta, Alpha, and Beta \(L_{\text{real}}\) was slightly higher in the high attention state (Theta, Alpha, Beta), while the Gamma band exhibited the largest discrepancy between the two attention states. Binary matrix comparisons, shown in Figure 10, revealed complex network structures across various brain regions [25]. Figure 8: Weighted network graph illustrating neural synchronization patterns across Theta, Alpha, Beta, and Gamma bands for high attention (HA) and low attention (LA) states. Key network metrics such as clustering coefficient, path length, and network degree depicted in Table 2 distinct variations, particularly within the Beta and Gamma bands. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Theta**} & \multicolumn{2}{c}{**Alpha**} & \multicolumn{2}{c}{**Beta**} & \multicolumn{2}{c}{**Gamma**} & \multirow{2}{*}{\(p\)**-Value**} \\ \cline{2-2} \cline{5-9}  & **HA** & **LA** & **HA** & **LA** & **HA** & **LA** & **HA** & **LA** & \\ \hline CC & 0.7767 & 0.7337 & 0.8322 & 0.6636 & 0.8695 & 0.7899 & 0.8650 & 0.8270 & 0.06 \\ PL & 1.3070 & 1.3643 & 1.1636 & 1.3135 & 1.1269 & 1.1773 & 1.1528 & 1.2123 & 0.04 \\ C\_real & 1.1184 & 1.0754 & 0.9995 & 1.1095 & 0.1148 & 1.0291 & 1.0379 & 0.9997 & 0.06 \\ PL\_real & 1.0005 & 1.0235 & 1.0004 & 1.0005 & 0.9996 & 1.0000 & 1.0004 & 1.0046 & 0.03 \\ Degree & 15.53 & 14.73 & 20.87 & 15.20 & 22.47 & 20.27 & 21.33 & 19.13 & 0.02 \\ \hline \hline \end{tabular} Statistical significance (\(p<0.05\)) was assessed using paired \(t\)-tests with Bonferroni correction applied for multiple comparisons.  
\end{table} 
Table 2: Comparison of HA and LA w.r.t. different network parameters. Figure 10: Binary matrices of the averaged non-linear synchronization matrices for high and low attention states across Theta, Alpha, Beta, and Gamma frequency bands. Individual thresholds are set, and the strength of the synchronization is represented using a black-gray scale, where yellow indicates \(H=0\) and black indicates \(H=1\). Figure 9: (**a**) Averaged normalized ratio of \(L_{\text{real}}\) (path length) to \(C_{\text{real}}\) (clustering coefficient) across Theta, Alpha, Beta, and Gamma bands, plotted against thresholds (0.01-0.6). (**b**) F-Score values for network metric parameters in Theta, Alpha, Beta, and Gamma bands. Statistical significance was assessed using paired \(t\)-tests with Bonferroni corrections. These metrics provide insights into the neural network's structure and function, highlighting differences in connectivity patterns under varying cognitive states. The bar graph in Figure 11 illustrates the comparison of global efficiency, small-worldness (SWN), and network density between high attention (HA) and low attention (LA) task conditions across Theta, Alpha, Beta, and Gamma frequency bands. Error bars emphasize significant differences (\(p<0.05\)) between conditions. These results underscore robust differences in network topology and efficiency between HA and LA states, emphasizing the impact of attentional states on cognitive network organization. The results reveal distinct neural synchronization patterns across attentional states. HA networks show higher global efficiency and small-worldness, indicating enhanced information transfer and a better balance between local specialization and global integration, particularly noticeable in the Theta and Gamma bands.