Analyzing Brain Activity During Learning Tasks with EEG and Machine Learning
This work provides insights into brain mechanisms during learning for researchers in neuroscience and education, but it is incremental as it applies existing methods to new data.
This study tackled the problem of classifying brain activity during STEM tasks using EEG data and machine learning, achieving a testing accuracy of 91.07% with Random Forest and identifying specific brain regions linked to different cognitive functions.
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.