# EEG-Based Attention Classification for Enhanced Learning Experience
## 1 Introduction  
Rapid developments in neurotechnology and machine learning have opened new possibilities for improving digital learning environments. However, a persistent challenge in education remains: traditional instructional systems often treat learners as homogenous,static entities, neglecting the moment-to-moment cognitive fluctuations--especially attentional variations--that are critical for effective learning. This gap is particularly pronounced in cognitively demanding domains such as second-language acquisition, where sustained focus significantly impacts memory encoding and retention [1]. Despite the availability of EEG (electroencephalography) as a real-time brain monitoring tool [2; 3], most existing learning systems that incorporate EEG signals fall short in several areas. They either rely on passive monitoring, analyze data offline, or lack real-time feedback loops that could personalize the learning experience. Moreover, they often treat attention as a unidimensional signal, overlooking the complex inter-regional brain dynamics that underlie attentional states [4; 5]. Recent studies have attempted to detect attentional states from EEG data, using techniques ranging from frequency band power analysis to machine learning classifiers [6; 7; 8]. However, these efforts typically lack integration across four critical dimensions: (1) optimized frequency band selection tailored to cognitive states; (2) real-time instructional adaptation based on EEG-derived attention levels; (3) brain connectivity modeling using graph-theoretic metrics like Phase Lag Index (PLI); and (4) interpretable classification methods such as k-NN that offer both accuracy and transparency. To address these limitations, this study presents a fully integrated, EEG-based neuroadaptive learning system designed for Mandarin vocabulary acquisition. The proposed framework dynamically adjusts task difficulty based on real-time classification of attention into HA and LA states. The training phase uses quiz performance to label EEG segments, followed by wavelet-based feature extraction and artifact removal. These features are classified using a k-NN model, and connectivity patterns are captured via Hilbert transform to generate functional correlation matrices [9; 10]. A distinctive contribution of this work lies in its use of thresholded functional brain networks to extract graph features, enabling deeper insights into attentional dynamics. While graph metrics such as clustering coefficient and path length have been used in other fields [9; 11], they remain underutilized in adaptive education systems. By incorporating these features into the attention classification pipeline, our system offers a network-informed approach that goes beyond simple band-power metrics. During real-time deployment, the system detects HA and LA states and modulates task difficulty accordingly--reducing cognitive load during LA and increasing challenge during HA to sustain engagement. This closed-loop feedback mechanism represents a novel application of adaptive learning aligned with cognitive neurodynamics. Related Work  
EEG-based attention monitoring has gained traction in educational neuroscience due to its ability to reveal real-time cognitive states. [PERSON] et al. [6] proposed an alpha-band-based system for measuring learner engagement but lacked adaptive control. [PERSON] et al. [7] incorporated eye-tracking to guide tutoring strategies, though feedback remained passive and limited to specific frequency bands. Supervised learning methods have been extensively applied to decode attentional states from physiological data. For example, [PERSON] et al. [12] employed the k-nearest neighbors (k-NN) algorithm for binary workload classification, while [PERSON] et al. [8] and [PERSON] et al. [13] utilized deep learning models such as long short-term memory (LSTM) networks. However, these approaches often suffer from limited interpretability and lack integration with adaptive feedback mechanisms.