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Investigating brain activity patterns during learning tasks through EEG and machine learning analysis

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Abstract

The objective of this study was to analyze brain activity during different STEM (Science, Technology, Engineering, and Mathematics) activities in order to better understand brain activity and the feasibility to classifying between various activities. Electroencephalogram (EEG) brain data from twenty subjects were collected during the engagement of five distinct cognitive tasks (working memory, planning, arithmetic, functioning, mental flexibility, and cognitive flexibility). This data was then segmented into 4 s clips and analyzed by taking the power spectral densities of brain frequency waves. After testing numerous different training and testing k-intervals between the XGBoost, Random Forest, and Bagging Classifier, it was found that the method of using the Random Forest performed the highest at 91.07% testing accuracy during an interval size of two. When all four EEG channels work together during classification, cognitive flexibility was most easily recognizable. However, after comparing each task’s classification performance on singular locations of sensors, it was found that the right frontal lobe provided high classification accuracy toward mathematical processing and planning, the left frontal lobe performed well on cognitive flexibility and mental flexibility, and the left temporoparietal lobe was best during connections. It was also found that there are lots of connections between the frontal and temporoparietal lobes during STEM activities. Ultimately, this study establishes a better understanding for the implementation of machine learning in brain activity and helps to better understand the brain’s mechanisms.

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Data availability

The data that support the findings of this study are available on request from the corresponding author, Dr. Hwang.

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Acknowledgements

We acknowledge the support from students at NIU who participated in the test as subjects.

Funding

This study was partially supported by 2023 UFA funding at NIU.

Author information

Authors and Affiliations

  1. Illinois Mathematics and Science Academy, Aurora, IL, USA

    Ryan Cho

  2. Department of Industrial and Systems Engineering, Northern Illinois University, DeKalb, IL, USA

    Mobasshira Zaman & Jaejin Hwang

  3. Mechanical Engineering Department, Northern Illinois University, DeKalb, IL, USA

    Kyu Taek Cho

Authors
  1. Ryan Cho
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  2. Mobasshira Zaman
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  4. Jaejin Hwang
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Contributions

Conceptualization: Jaejin Hwang, Ryan Cho; Methodology: Mobasshira Zaman, Ryan Cho; Formal analysis and investigation: Ryan Cho; Writing—original draft preparation: Ryan Cho; Writing—review and editing: Jaejin Hwang, Kyu Taek Cho, Ryan Cho; Funding acquisition: Jaejin Hwang, Kyu Taek Cho; Resources: Jaejin Hwang; Supervision: Jaejin Hwang.

Corresponding author

Correspondence to Jaejin Hwang.

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The authors declare that they have no financial or non-financial interests that are directly or indirectly related to the work submitted for publication.

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Cho, R., Zaman, M., Cho, K.T. et al. Investigating brain activity patterns during learning tasks through EEG and machine learning analysis. Int. j. inf. tecnol. (2024). https://doi.org/10.1007/s41870-024-01856-4

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