Exploring Online Learning Status Based on EEG Data Analysis

With the widespread application of online learning in the field of education, conducting in-depth research on students' online learning status is crucial for enhancing educational quality. Electroencephalogram (EEG) signal analysis emerges as a potential method that can provide valuable reference information for the online education sector. In this study, firstly, a combined approach of time-domain and frequency-domain methods was employed to extract features from EEG signals. Time-domain features can reveal the temporal characteristics of EEG signals, while frequency-domain features offer information on energy distribution across different frequency bands. Subsequently, a Support Vector Machine (SVM) classifier model was established to screen channels, selecting those with superior classification performance. Concurrently, a feature effectiveness screening process was conducted to retain features that contribute to the classification of students' online learning status. To further enhance classification performance, algorithms such as K-Nearest Neighbors (KNN) and Decision Trees were also selected for comparative analysis, and parameter adjustments were made to identify the optimal classifier settings. These experiments aimed to validate the effectiveness of features and channels and improve the accuracy of online assessment of students' learning status.