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.

该研究旨在分析不同 STEM 活动中的大脑活动，探索分类不同任务的可行性。收集了二十个受试者在五个认知任务中的脑电数据，并将其分割为 4 秒的片段。通过分析大脑频率波的功率谱密度，测试了使用 XGBoost、随机森林和装袋分类器等不同的 k 间隔，结果显示随机森林在两个间隔大小时表现最好，测试准确率达到 91.07%。当利用所有四个脑电通道时，认知灵活性最为显著。任务特定的分类准确率显示右额叶在数学处理和规划方面表现出色，左额叶在认知灵活性和心理灵活性方面表现出色，左颞顶叶在连接方面表现出色。值得注意的是，在进行 STEM 活动时观察到了额叶和颞顶叶之间的许多连接。这项研究有助于更深入地了解将机器学习应用于分析大脑活动，并揭示了大脑的机制。