# Analyzing Brain Waves of Table Tennis Players with Machine Learning for Stress Classification
## 3 Results and Discussion
### Performance Evaluation of Classification Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Acc** & **Pre** & **Rec** & **F1** & **MCC** \\ \hline C4.5 & 0.8631 & 0.8711 & 0.8530 & 0.8616 & 0.7261 \\ CART & 0.8549 & 0.8565 & 0.8538 & 0.8551 & 0.7092 \\ LR & 0.8380 & 0.8502 & 0.8222 & 0.8357 & 0.6762 \\ RF & 0.9029 & 0.9171 & 0.8859 & 0.9010 & 0.8061 \\ SVM & 0.9005 & 0.9142 & 0.8840 & 0.8986 & 0.8013 \\ XGBoost & **0.9427** & **0.9485** & **0.9365** & **0.9424** & **0.8852** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Generalizing model for the two-level stress classification (high and low stresses). The results in Table 6 indicate that, among the 25 players, the one with the best accuracy was as high as 99.44%. In this study, whether with the generalizing model or with the personalizing model, the best algorithm was always XGBoost. The three-level stress classification of the generalizing model had an accuracy of 86.49%. With the same model, the two-level stress classification between high and low stresses had an accuracy of 94.27% or an accuracy of 92.43% for the mid and low stresses. The three-level stress classification of the personalizing model had an average accuracy of 90.77%, with a top individual player hitting 99.44%. The personalizing model had higher accuracies than the generalizing model, because the instability of an individual's brain waves, in addition to differences between the individuals, leads to lower accuracies in the classification with the generalizing model. Given that brain waves vary in individuals, if we could employ algorithms to identify the causes of those variances and eliminate them, we would be able to improve the accuracy of the classification with the generalizing model. We compared the schemes proposed by past studies and the present one based on the number of subjects, channels, bands, features, stressors, classifier, and accuracy, as Table 7 shows. We had the most subjects; yet, there were still under 30; it will be an option in the future to increase the number of subjects to make samples more representative and more powerful as evidence. For the classifier in this study, we adopted a relatively new algorithm of machine learning, XGBoost, whilst, in most of the other studies, the SVM classifier was used in the assessment. In this study, XGBoost always outperformed the SVM, whether with the generalizing model or with the personalizing model. Last, the three-level classification \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Acc** & **Pre** & **Rec** & **F1** \\ \hline
1 & **0.9944** & **0.9952** & **0.9944** & **0.9944** \\
2 & 0.9833 & 0.9878 & 0.9806 & 0.9822 \\
3 & 0.9830 & 0.9827 & 0.9819 & 0.9804 \\
4 & 0.9722 & 0.9746 & 0.9724 & 0.9710 \\
5 & 0.9667 & 0.9660 & 0.9699 & 0.9638 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Personalizing model—GXBoost three-level stress classification (top five individuals). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Subjects** & **Channels** & **Bands** & **Features** & **Stressors** & **Classifier** & **Accuracy** \\ (Year) & & & & & & & (Classes) \\ \hline
[16] & & & & & & & \\ (2019) & 24 & 2 & 4 & 4 & SCWT & CNN & 87.5\% (2) \\ \hline
[17] & & & & & & & \\ (2019) & 14 & 2 & 4 & 8 & & & SVM & 77.9\% (2) \\ \hline
[18] & & & & & & & \\ (2018) & 1 & 14 & 4 & 56 & MA & RF & 96.0\% (2) \\ \hline
[19] & & & & & & & \\ (2017) & 22 & 19 & 10 & 180 & MA & NB & 94.6\% (2) \\ \hline
[20] & & & & & & & \\ (2017) & 9 & 1 & 4 & 13 & MA & SVM & 86.66\% (2) \\ \hline
[21] & & & & & & & \\ (2016) & 10 & 14 & 3 & 52 & & & \\ (2015) & 9 & 14 & 3 & 10 & SCWT & SVM & 85.17\% (2) \\ \hline This study & 25 & 8 & 8 & 64 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: A comparison of this study and past studies. of this study had an accuracy of 86.49%, which excelled compared to other studies by as far as 11.27% in the three-level classification.