Analyzing Brain Activity During Learning Tasks with EEG and Machine
  Learning
Ryan Cho Mobasshira Zaman Kyu Taek Cho Jaejin Hwang
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.