# A Game Player Expertise Level Classification System Using Electroencephalography (EEG)
## 3 Experimental Results
### Performance Analysis  
The performance of these algorithms is evaluated using different performance metrics for both cases, i.e., by using data from all fourteen EEG electrodes and those selected channels that give higher activity. The performance parameters include classification accuracy, computation time, kappa statistic, precision, recall and area under the curve (AUC) of the receiving operator characteristic (ROC) curve. [PERSON]'s kappa statistic is used to measure the inter-observer agreement and has a value that ranges between zero and one. A value close to zero represents agreement by chance, and a value closer or equal to one represents near perfect or perfect agreement. For a classification task, precision is defined as,  
\[precision=\frac{TP}{TP+FP}, \tag{18}\]  
where \(TP\) and \(FP\) represent the true positive and false positive values, respectively. The recall value is calculated as,  
\[recall=\frac{TP}{TP+FN}, \tag{19}\]  
where \(FN\) represents the false negative values. The AUC of the ROC curve represents the classification performance of a classifier. A value closer to one shows that the classifier has a good performance. The results are presented in Table 2 and show an accuracy of 88% for the naive Bayes classification when data from all fourteen available channels are used. Moreover, the naive Bayes algorithm classified the data in 0.03 seconds. The kappa statistic of 0.6218 is achieved by both the SVM and MLP, which is 0.1 greater than the naive Bayes classifier. On the other hand, MLP takes the longest time to build when the fourteen channel data are used. Table 2 also shows the results for the selected performance parameters when four significant channels are selected. It is evident that the performance parameters of the four-channel classification have a slight difference as compared to the fourteen-channel case. In particular, the classification accuracy improves for SVM and MLP and is slightly reduced for naive Bayes. The time taken for all algorithms is now reduced to acceptable levels for real-time applications except for MLP, which is still comparatively higher. SVM gives the best classification accuracy of 86% with a classification time of 0.04 s with four channels. This shows that by selecting the significant channels, both SVM and naive Bayes can be reliably used for player classification in less time. Figure 8 shows the performance of the algorithms used in terms of mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE) and root relative squared error (RRSE) using fourteen and four channels. The MAE is calculated using,  
\[MAE=\frac{\sum_{i=1}^{n}|o_{i}-b_{i}|}{n}, \tag{20}\] \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Number of Channels** & **Classification Algorithm** & **Correctly Classified** & **Incorrectly Classified** & **Time Taken (s)** & **kappa Statistics** & **Precision** & **Recall** & **ROC** \\ \hline \multirow{3}{*}{4 channels} & Naive Bayes & 84 & 16 & 0.01 & 0.6543 & 0.839 & 0.84 & 0.881 \\  & SVM & 86 & 14 & 0.04 & 0.6998 & 0.859 & 0.860 & 0.846 \\  & MLP & 84 & 16 & 0.94 & 0.6604 & 0.840 & 0.840 & 0.900 \\ \hline \multirow{3}{*}{14- channels} & Naive Bayes & 88 & 12 & 0.03 & 0.7356 & 0.886 & 0.880 & 0.899 \\  & SVM & 82 & 18 & 0.10 & 0.6218 & 0.822 & 0.820 & 0.821 \\ \cline{1-1}  & MLP & 82 & 18 & 7.5 & 0.6218 & 0.822 & 0.820 & 0.907 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance parameters of classification algorithms using the four selected channels and all fourteen available channels. where \(n\) is the number of observations and \(o_{i}\) and \(b_{i}\) are the observed and actual values, respectively. The RMSE is calculated using,  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(o_{i}-b_{i}\right)^{2}}{n}}, \tag{21}\]  
The RAE is calculated using,  
\[RAE=\frac{\sum_{i=1}^{n}\left|o_{i}-b_{i}\right|}{\sum_{i=1}^{n}\left|\sigma_{ i}-b_{i}\right|}, \tag{22}\]  
where \(\sigma_{i}\) is the mean of \(o_{i}\).