# Effect of Subject-Specific Region of Interest on Motor Imagery Brain-Computer Interface
## 3 Results and Analysis
### Comparison of ITD Technique with DWT without ROI Estimation  
During motor imagery tasks, changes in the state of the brain lead to changes in EEG frequency and amplitude. Thus, for a clearer understanding of a nonlinear model of ITD to better map the signals into one of the four classes, the ITD approach was compared and validated using discrete wavelet transform (DWT). The comparative classification results of ITD and DWT without ROI are illustrated in Figure 8. On average, for the 15 subjects, the mean classification accuracy obtained was 78.39% for DWT and 86.80% for ITD. From Figure 8, it is clearly seen that the ITD increased the average classification accuracy not only for the 15 subjects but also for the individual subject, as evidenced by the smaller error in the boxplot. The percentage increase in classification accuracy for ITD was 9.68% compared to that of DWT. This change was found to be significant at a level of \(p\) < 0.001. The ITD method drastically improves the accuracy of the system, as well as the capabilities of this method to separate among the four classes of motor imagery movements compared to the equivalent decomposition method of DWT without ROI. In summary, the improvements brought upon by ITD are mainly due to better handling of critical cases [35].  
Figure 9 shows the boxplot of the mean classification results for sensitivity. The average classification sensitivity for ITD and DWT was 86.14% and 78.39%, respectively. The ITD sensitivity improved by 8.99% compared to that of DWT. This change was significant at \(p\) < 0.001.  
Figure 8: The accuracy of ITD and DWT for 15 subjects.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Predicted Class**} \\ \cline{2-6}  & & **Right Hand** & **Left Hand** & **Both Hands** & **Both Feet** \\ \cline{2-6} True class & Right hand & 82.94\% & 7.53\% & 5.71\% & 3.82\% \\ Left hand & 13.65\% & 79.01\% & 4.82\% & 2.53\% \\ Both hands & 11.53\% & 9.83\% & 76.03\% & 2.61\% \\ Both feet & 10.01\% & 5.07\% & 4.84\% & 80.08\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix of ITD without ROI.  
Figure 10 shows the boxplot of the specificity for ITD and DWT. The mean classification specificity for ITD and DWT was 87.73% and 79.05%, respectively. Compared to the specificity of DWT, the ITD specificity increased by 9.89%; this change was significant at \(p<0.001\).  
Figure 11 shows the boxplot of the kappa coefficient classification for ITD and DWT. The average kappa coefficients for ITD and DWT were 0.8011 and 0.7042, respectively.  
Figure 10: The specificity of ITD and DWT for 15 subjects.  
Figure 9: The sensitivity of ITD and DWT for 15 subjects.  
The comparative results of ROCs between ITD and DWT are shown in Figure 12. The green line shows the results of the ITD method, while the red line shows the results of the DWT method. The classification accuracy of ITD was 86.80%, with a sensitivity of 84.14% and a specificity of 87.73%. The classification accuracy for DWT was 78.58%, the sensitivity was 78.39% and the specificity was 79%. A comparison between the ITD and DWT decomposition methods showed that the ITD algorithm outperformed DWT by showing a higher accuracy of feature classifications for different motor imagery tasks. The DWT algorithm is sensitive to wavelet function and wavelet level. DWT has more difficulties in treating both hands' classes. On the other hand, the effect of this problem on the ITD approach does not seem to be too significant. This is mainly due to better handling of critical cases, resulting in a 9.47% improvement in classification accuracy of both hands' classes. DWT lacks fast algorithms; it takes up to 12.83 times longer compared to ITD.  
Figure 11: The kappa coefficient of ITD and DWT for 15 subjects.  
Figure 12: The ROCs of sensitivity vs. 1-specificity for ITD and DWT.