# EEG-Based Attention Classification for Enhanced Learning Experience
## 4 Discussion  
This study presents a comprehensive investigation into attentional dynamics during second-language learning using an EEG-based neuroadaptive system. By emphasizing gamma-band EEG features and integrating graph-theoretic metrics, the proposed framework offers both theoretical and practical advancements over traditional and prior EEG-based learning systems. Gamma activity is known to reflect attentional binding and memory integration, likely explaining its superior performance. Unlike previous neuro-feedback models that primarily focused on alpha-band oscillations. Our system targets gamma-band activity, which is more directly associated with active cognitive processing and sustained attention.This frequency-specific tuning allowed for finer discrimination \begin{table}
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**Metric** & **Before EAC Adjustment** & **After EAC Adjustment** \\ \hline Average Correct Answers (\%) & 71\% & 89\% \\ \hline Standard Deviation (\%) & 15\% & 10\% \\ \hline \hline \end{tabular}
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Table 5: Quiz performance before and after EAC adjustment. Figure 13: Boxplots illustrating quiz performance scores, response times, and EEG-derived attention levels under low and high attention conditions, along with normalized values of neural connectivity metrics across conditions. \begin{table}
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**Aspect** & **Details** & & \\ \hline Model Name & EEG Adaptive Classification (EAC) Model & \\ Purpose & Real-time quiz difficulty adjustment based on attention levels & \\ Performance Metric & Classification accuracy, precision, recall, and F1-Score & \\ \hline \hline
**Attention State** & **Performance Metrics** & & \\ \hline High Attention (HA) & Accuracy: 87\% & Precision: 91\% & Recall: 87\% & F1-Score: 88\% \\ Low Attention (LA) & Accuracy: 87\% & Precision: 83\% & Recall: 85\% & F1-Score: 84\% \\ \hline \hline \end{tabular}
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Table 4: Details of the EEG adaptive classification model. between HA and LA states, enabling more precise real-time instructional adjustments. The classifications were achieved through a hybrid approach involving wavelet-based feature extraction, PCA for dimensionality reduction, and interpretable machine learning K-NN classifier. A key innovation of this work lies in the integration of graph-theoretic features derived from thresholded functional connectivity matrices, obtained via the Hilbert transform and Phase Lag Index (PLI). Metrics such as clustering coefficient and path length [17] captured the small-world properties of neural networks during varying attentional states. This network-informed approach enhanced interpretability and provided a neurophysiological basis for adaptive feedback mechanisms. Our system also improves upon black-box deep learning models by offering transparency and physiological plausibility. Unlike models that optimize only for accuracy, our framework is grounded in cognitive theory, specifically aligning instructional changes with inferred attentional states in a closed-loop architecture. For instance, flashcard difficulty was reduced in LA states to avoid disengagement and increased during HA to maximize learning gains--realizing a dynamic adaptation strategy rooted in cognitive load theory. Compared to recent neuroadaptive proposals [17], this work is distinctive in its simultaneous application of gamma-band analysis, functional brain network features, and real-time feedback deployment. Moreover, preprocessing techniques such as artifact removal and wavelet denoising preserved signal fidelity and minimized the influence of ocular or muscle artifacts [26]. PLI-based connectivity analyses validated the hypothesis that high attention is associated with more efficient brain network topologies [14], providing both a diagnostic tool and a feedback mechanism. Taken together, these contributions mark a significant advancement in adaptive educational technologies.