# EEG-Based Attention Classification for Enhanced Learning Experience
## 2 Materials and Methods  
### Task Design and Stimuli  
Nineteen healthy, right-handed adults aged 25-40 participated, all with beginner-level (CEFR A1) Chinese proficiency assessed using a standardized language proficiency test. Participants' native languages included English (70%), Urdu (20%), and other languages (10%). None had prior formal training in Chinese or exposure to phonetic systems such as Pinyin.  
Motivational levels and visual memory were assessed using pre-task surveys. Only participants scoring above 60% on the engagement scale were included to minimize variability. The task was implemented using STIM software and followed a methodology adapted from previous research [19]. The experiment assessed attention by combining active learning phases with subsequent recall tasks as shown in Figure 2. The Ethics Committee of the Institute approved this study, and all subjects gave written informed consent before beginning the experiment.  
Participants were exposed to Chinese vocabulary words under two experimental conditions: (i) Chinese characters presented with Pinyin to assist pronunciation, and (ii) Chinese characters presented without Pinyin, requiring participants to memorize pronunciation and meaning without phonetic support. Each learning session was followed by a recall phase to assess memory retention and attention levels.  
Key performance measures included:  
* **Response accuracy:** Correct answers during recall were considered indicative of high attention (HA), while incorrect answers signaled low attention (LA).  
A comprehensive overview of the data analysis process is illustrated in Figure 3. This figure outlines each step from EEG signal acquisition to feature extraction and classification.  
The EEG signals were recorded using a 30-channel electrode setup following the international 10-20 system. The specific electrode locations included: Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T7, T8, P7, P8, Fz, Cz, Pz, Oz, FC1, FC2, CP1, CP2, FC5, FC6, CP5, CP6, PO3, and PO4.  
Figure 2: 32-channel EEG recording setup using a Quik-Cap system. (a) Electrode placement according to the international 10–20 system for comprehensive brain activity monitoring. (b) EEG signal acquisition during sequential experimental conditions: rest, learning, rest, testing, and rest phases.  
Data acquisition was carried out using a 30-channel Brain-Computer Interface (BCI) system equipped with silver/silver chloride (Ag/AgCl) electrodes. These electrodes were selected for their high signal fidelity and low noise characteristics, ensuring reliable measurements for attention-related EEG analysis [19]. Electrode impedance was maintained below 5 k\(\Omega\) using a built-in impedance meter integrated into the acquisition system, with conductive gel applied to ensure proper connectivity [9; 19].  
Figure 4 shows the denoised EEG signal from channel P3 during high attention (HA) and low attention (LA) conditions. The EEG signals were sampled at 1000 Hz to achieve a balance between high temporal resolution and computational efficiency, enabling the detection of neural activity up to 100 Hz. Baseline correction was applied using the \(-100\) to 0 ms prestimulus interval to eliminate low-frequency drifts, as commonly required in event-related EEG analyses.  
The details of our preprocessing pipeline based on the EEGLAB toolbox [20] are given as follows: