Analyzing Brain Activity During Learning Tasks with EEG and Machine Learning

TL;DR

This study demonstrates that machine learning, particularly Random Forest, can classify different STEM learning tasks from EEG data with over 91% accuracy, revealing brain region involvement and interconnections.

Contribution

It introduces a method for classifying cognitive tasks using EEG and machine learning, highlighting specific brain regions and connections involved in STEM activities.

Findings

Random Forest achieved 91.07% accuracy in task classification.

Different brain regions are associated with specific cognitive functions.

Interconnections between frontal and temporoparietal lobes are prominent during tasks.

Abstract

This study aimed to analyze brain activity during various STEM activities, exploring the feasibility of classifying between different tasks. EEG brain data from twenty subjects engaged in five cognitive tasks were collected and segmented into 4-second clips. Power spectral densities of brain frequency waves were then analyzed. Testing different k-intervals with XGBoost, Random Forest, and Bagging Classifier revealed that Random Forest performed best, achieving a testing accuracy of 91.07% at an interval size of two. When utilizing all four EEG channels, cognitive flexibility was most recognizable. Task-specific classification accuracy showed the right frontal lobe excelled in mathematical processing and planning, the left frontal lobe in cognitive flexibility and mental flexibility, and the left temporoparietal lobe in connections. Notably, numerous connections between frontal and temporoparietal lobes were observed during STEM activities. This study contributes to a deeper understanding of implementing machine learning in analyzing brain activity and sheds light on the brain's mechanisms.