# Analyzing Brain Waves of Table Tennis Players with Machine Learning for Stress Classification
## 4 Conclusions  
In this study, the EEG of table tennis players was collected by using the SCWT and MA and analyzed by algorithms of machine learning, namely, LR, SVM, C4.5, CART, RF, and XGBoost; our experiment revealed XGBoost as the optimal model. The stress models included the generalizing model and personalizing model, both providing the analytic results of stresses on players. This study outperformed other studies by up to 11.27% in the three-level classification. This study provided optimal classification results, which makes it different from other studies. The number of players in this study was limited, and it is hoped to expand the samples of data in the future to at least 30 so that the samples will be more representative and more powerful as evidence, while the stress models will be generalizing in a better way. As brain waves vary from individual to individual, if we could utilize algorithms to identify the causes of such variations, we will improve the accuracies of classification with generalizing models. XGBoost has a good accuracy for low-dimensional feature data, but it is not suitable for processing high-dimensional feature data. In the future, it can be combined with deep learning, using deep learning to extract features and then using XGBoost for classification as the two are combined into a new model. The chief key feature of stress classification is High Gamma at the O1 electrode position, and this feature can serve as an important key feature in future research on psychological stresses. Last, this study employed the two different stressors of the SCWT and MA in the experiment, whereas the data from the lab context were limited and differed from those of the competition context that table tennis players face. It is possible in the future to collect data on brain waves during training or games to build stress models in competition contexts and to compare the differences thereof. Conceptualization, Y.-H.T, S.-S.Y. and M.-H.T. ; Data curation, Y.-H.T. ; Formal analysis, Y.-H.T. and S.-S.Y. ; Funding acquisition, S.-K.W. ; Investigation, Y.-H.T. ; Methodology, S.-S.Y. and M.-H.T. ; Project administration, Y.-H.T. and M.-H.T. ; Resources, S.-K.W. and M.-H.T. ; Software, Y.-H.T. ; Supervision, S.-S.Y. and M.-H.T. ; Validation, Y.-H.T. and M.-H.T. ; Visualization, Y.-H.T. ; Writing--original draft, Y.-H.T. ; and Writing--review and editing, Y.-H.T., S.-K.W. and M.-H.T. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compare** & **High Stress** & **Mid Stress** & **Low Stress** & **\(t\)-Test** \\
**Categories** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **\(\alpha\) = 0.05** \\ \hline High, Mid & 2.36 \(\pm\) 1.31 & 1.85 \(\pm\) 0.95 & - & \(t\) = 12.16, \\  & & & \(p\) < 0.001 \\ \hline Mid, Low & - & 1.85 \(\pm\) 0.95 & 1.42 \(\pm\) 0.76 & \(t\) = 13.72, \\  & & & \(p\) < 0.001 \\ \hline High, Low & 2.36 \(\pm\) 1.31 & - & 1.42 \(\pm\) 0.76 & \(t\) = 24.15, \\  & & & \(p\) < 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Independent sample \(t\)-test on the O1_HighGamma features. **Funding:** This research was funded by the Ministry of Science & Technology, R.O.C., grant number MOST109-2627-H-028-003. **Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of the First Human Research Ethics Review Board affiliated with the College of Medicine, National Cheng Kung University (A-ER-108-041, 22 March 2019) for studies involving humans. **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. Written informed consent was obtained from the patient(s) to publish this paper. **Data Availability Statement:** The labeled datasets used to support the findings of this study are available from the corresponding author upon request.