# EEG analysis of brain dynamics in a simulated multi-task and multi-stage learning environment

## TL;DR

This study used EEG to track brain activity in a simulated online biology course, finding distinct patterns linked to different learning stages and tasks.

## Contribution

The study introduces a novel approach to analyzing brain dynamics in a realistic, multi-stage learning environment using EEG.

## Key findings

- Frontal theta activity increased during quizzes, parietal alpha suppression occurred during lectures, and high-beta activity rose in later stages.
- Machine learning models achieved 83% accuracy in classifying learning stages based on EEG features.
- Results suggest brain activity patterns vary significantly across learning tasks and stages.

## Abstract

The development of brain oscillation patterns during knowledge acquisition has gained attention, yet studies in realistic learning contexts remain limited. This study investigated dynamic brain activity across an 11-lesson biology course simulating a MOOC environment. Twenty undergraduates wore 14-channel Electroencephalography (EEG) headsets while completing lecture, virtual lab, and quiz tasks across three progressive stages. EEG signals from six participants (after quality screening) were analyzed using amplitude, power spectral density (PSD), and phase-locking index (PLI). Wilcoxon rank sum tests revealed significant stage- and task-related differences despite the small sample size, including increased frontal theta during quizzes, parietal alpha suppression during lectures, and high-beta enhancements in later stages of labs and quizzes. Machine learning models trained on EEG features achieved a classification accuracy of 83% for three learning stage discrimination, validating that the brain presents nonidentical functional patterns during cognitive learning. These results underscore the potential for real-time EEG-based personalized educational interventions.