2024/01/15 by Ryan Cho, Cho, Ryan, Mobasshira Zaman +5
Neuroscience · #EEG and Brain-Computer Interfaces #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Neurons and Cognition (q-bio.NC) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2401.10285
openalex publication_date 2024/01/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.