Analyzing Brain Waves of Table Tennis Players with Machine Learning for Stress Classification Article Swipe
Yu-Hung Tsai
,
Sheng-Kuang Wu
,
Shyr-Shen Yu
,
Meng-Hsiun Tsai
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app12168052
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app12168052
Electroencephalography (EEG) has been widely used in the research of stress detection in recent years; yet, how to analyze an EEG is an important issue for upgrading the accuracy of stress detection. This study aims to collect the EEG of table tennis players by a stress test and analyze it with machine learning to identify the models with optimal accuracy. The research methods are collecting the EEG of table tennis players using the Stroop color and word test and mental arithmetic, extracting features by data preprocessing and then making comparisons using the algorithms of logistic regression, support vector machine, decision tree C4.5, classification and regression tree, random forest, and extreme gradient boosting (XGBoost). The research findings indicated that, in three-level stress classification, XGBoost had an 86.49% accuracy in the case of the generalized model. This study outperformed other studies by up to 11.27% in three-level classification. The conclusion of this study is that a stress detection model that was built with the data on the brain waves of table tennis players could distinguish high stress, medium stress, and low stress, as this study provided the best classifying results based on the past research in three-level stress classification with an EEG.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app12168052
- https://www.mdpi.com/2076-3417/12/16/8052/pdf?version=1660216244
- OA Status
- gold
- Cited By
- 18
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4290999911
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4290999911Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app12168052Digital Object Identifier
- Title
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Analyzing Brain Waves of Table Tennis Players with Machine Learning for Stress ClassificationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-08-11Full publication date if available
- Authors
-
Yu-Hung Tsai, Sheng-Kuang Wu, Shyr-Shen Yu, Meng-Hsiun TsaiList of authors in order
- Landing page
-
https://doi.org/10.3390/app12168052Publisher landing page
- PDF URL
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https://www.mdpi.com/2076-3417/12/16/8052/pdf?version=1660216244Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2076-3417/12/16/8052/pdf?version=1660216244Direct OA link when available
- Concepts
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Electroencephalography, Artificial intelligence, Support vector machine, Computer science, Preprocessor, Decision tree, Stress (linguistics), Stroop effect, Machine learning, Random forest, Pattern recognition (psychology), Speech recognition, Psychology, Cognition, Neuroscience, Linguistics, Psychiatry, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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18Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 8, 2023: 6, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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48Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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