# EEG-Based Attention Classification for Enhanced Learning Experience
## 1 Introduction
Graph-theoretic modeling of brain connectivity has been investigated in various domains, including deception detection and industrial safety [9; 11]. Small-world network metrics, which reflect an optimal balance between local specialization and global integration, have been shown to be essential for evaluating brain efficiency [14]. In the context of education, [PERSON] et al. [15] and [PERSON] et al. [16] introduced network-based features for classifying attention states. Nevertheless, their implementations were not embedded within closed-loop or neuroadaptive learning systems. Neuroadaptive systems that respond to attentional fluctuations are still emerging. [PERSON] et al. [17] presented an attention-aware deep learning model, but its interpretability and integration with educational content were limited. [PERSON] et al. [18] demonstrated real-time decoding but did not incorporate content adaptation or graph-based connectivity features. **In contrast**, our work integrates these critical components into a unified, real-time neuroadaptive system for language learning. It uniquely combines frequency-optimized EEG features, graph-theoretic connectivity metrics, and interpretable k-NN classification to adjust task difficulty dynamically based on HA and LA states. As shown in Figure 1, the EAC system captures and classifies real-time EEG signals to adapt flashcard difficulty levels during learning sessions. To the best of our knowledge, no prior work offers such an end-to-end, network-informed adaptive framework for second-language acquisition. **Key contributions of this paper include:**  
* Development of a real-time EEG-based neuroadaptive learning framework integrating frequency band selection, graph-theoretic connectivity, and interpretable classification. * Empirical validation showing gamma band superiority for HA/LA classification, supported by connectivity metrics. * Demonstration of improved engagement and learning efficiency through dynamic task adjustment based on real-time EEG feedback. * A practical implementation tailored for Mandarin vocabulary learning, with potential generalizability to broader educational contexts. By combining neuroscience, signal processing, and educational theory, this study advances the state of neuroadaptive learning and offers a scalable solution for intelligent tutoring systems. The remainder of this paper is structured as follows:  
Recent developments in EEG-based attention classification are reviewed in this section. The study's methodology, which includes feature extraction, preprocessing, k-nearest neighbors (k-NN) classification, and real-time neurofeedback technique, is described in  
Figure 1: System Design of the EEG-Based Attention Classification (EAC) Model. Section 2. Results on the efficiency of k-NN for real-time neurofeedback and attention classification are shown in Section 3, along with performance metrics. The findings' consequences for comprehending attention mechanisms, as well as their influence on pedagogical approaches and neurocognitive research, are covered in Section 4. Section 5 concludes the study, emphasizing how real-time modifications based on EEG data might enhance educational research and learning experiences.