ANALYZING EEG SIGNALS FOR STRESS DETECTION USING RANDOM FOREST ALGORITHM

Fi Imanur Sifaunnufus Ms, Fitra Abdurrachman Bachtiar, Barlian Henryranu Prasetio

Abstract


Detection of stress using EEG signals has gained much interest because of monitoring and early intervention. As for the contribution of this research, a reliable method for stress identification has been suggested, using a random forest model to categorize stress levels from EEG signals. Data were filtered using a bandpass filter, Independent Component Analysis, and more so using the Z-score to remove outliers and poor signals. Data that has been cleaned from noise and outliers will go through a feature extraction process using Power Spectral Density (PSD). The result of PSD is the power of each frequency of the EEG signal. The number of features used is 20. Random Forest was chosen due to its high accuracy and robustness in handling complex, high-dimensional data, which is common in EEG analysis. Thus, the model obtained an accuracy level of 0.8571, thereby approving the tool’s efficiency in distinguishing between different degrees of stress. The computational efficiency of the model, with a classification time of 0.2762 seconds, demonstrates its feasibility for practical applications. Based on these findings, it can be concluded that the Random Forest algorithm can be used to integrate wearable technology and for offering suggestions and timely interventions for better mental health.

Keywords


Stress; EEG; Random Forest; Machine Learning

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References


  1. O’Connor DB, Thayer JF, Vedhara K. Stress and Health: A Review of Psychobiological Processes. Annual Review of Psychology. 2021 Sep 4;72(1):663–88.
  2. Queensland Centre for Mental Health Research (QCMHR). Indonesia – National Adolescent Mental Health Survey (I-NAMHS) Report (Bahasa Indonesia) [Internet]. qcmhr.org. 2023. Available from: https://qcmhr.org/wp-content/uploads/2023/02/I-NAMHS-Report-Bahasa-Indonesia.pdf
  3. Arsalan A, Majid M, Butt AR, Anwar SM. Classification of Perceived Mental Stress Using A Commercially Available EEG Headband. IEEE Journal of Biomedical and Health Informatics. 2019 Nov;23(6):2257–64.
  4. Li R, Liu Z. Stress detection using deep neural networks. BMC Medical Informatics and Decision Making. 2020 Dec;20(S11).
  5. Priya TH, Mahalakshmi P, Naidu V, Srinivas M. Stress detection from EEG using power ratio. 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). 2020 Feb;
  6. Pandey P, Miyapuram KP. Nonlinear EEG analysis of mindfulness training using interpretable machine learning. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2021 Dec 9;
    1. Malviya L, Mal S, Lalwani P. EEG Data Analysis for Stress Detection. 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT). 2021 Jun 18;
    2. Tasyakuranti AN, Sumarti H, Kusuma HH, Istikomah I, Prastyo IS. ANALYSIS OF THE EFFECT OF ISTIGHFAR DHIKR TO ADOLESCENT ANXIETY AT BETA WAVE ACTIVITY USING ELECTROENCEPHALOGRAM (EEG) EXAMINATION. Jurnal Neutrino:Jurnal Fisika dan Aplikasinya [Internet]. 2022 Sep 26 [cited 2024 Jul 15];15(1):31–7. Available from: https://ejournal.uin-malang.ac.id/index.php/NEUTRINO/article/view/17270/9748
    3. Sen D, Bhupati Bhusan Mishra, Prasant Kumar Pattnaik. A Review of the Filtering Techniques used in EEG Signal Processing. 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI). 2023 Apr 11;
    4. Tharwat A. Independent component analysis: An introduction. Applied Computing and Informatics. 2018 Aug;17(2).
    5. Zhou R, Han J, Li T, Guo Z. Fast Independent Component Analysis Denoising for Magnetotelluric Data Based on a Correlation Coefficient and Fast Iterative Shrinkage Threshold Algorithm. IEEE transactions on geoscience and remote sensing. 2022 Jan 1;60:1–15.
    6. Rousseeuw PJ, Hubert M. Robust statistics for outlier detection. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2011 Jan;1(1):73–9.
    7. Yi Wen T, Mohd Aris SA. Electroencephalogram (EEG) stress analysis on alpha/beta ratio and theta/beta ratio. Indonesian Journal of Electrical Engineering and Computer Science. 2020 Jan 1;17(1):175.
    8. Loza CA, Principe JC. EEG Models and Analysis. Springer eBooks. 2021 Jan 1;1–36.
    9. Su C. Heart Rate Variability Feature Selection using Random Forest for Mental Stress Quantification [Internet]. spectrum.library.concordia.ca. 2020. Available from: https://spectrum.library.concordia.ca/id/eprint/987471/
    10. Mohammad Zoynul Abedin, M. Kabir Hassan, Hajek P, Mohammed Mohi Uddin. The Essentials of Machine Learning in Finance and Accounting. Routledge; 2021.
    11. Tangirala S. Evaluating the Impact of GINI Index and Information Gain on Classification using Decision Tree Classifier Algorithm. International Journal of Advanced Computer Science and Applications. 2020;11(2).
    12. Boateng EY, Otoo J, Abaye DA. Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review. Journal of Data Analysis and Information Processing. 2020;08(04):341–57.




DOI: https://doi.org/10.18860/neu.v17i1.28471

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