## 4 Experimental Studies
### Results
Hence, a band pass filter is used to eliminate the insignificant bands and only obtain the significant sections during the Event Related Synchronisation (ERS) and Event Related De-synchronisation (ERD). Hence, the frequency range is selected between 5 and 30 Hz and the selected points are 5,8,12, 14, 20, 24, and 30. For the subject specific band with FDA type, the F-score is computed for all of the ranges of the frequency-time region for identifying the optimal parameters and the maximum F-score. As the subject specific results are obtained, the optimal time-frequency area is identified for each individual test subject along with the subject-specific TDP features for training the classifier. Multiple class LDA is used for the classification for the subject specific results. The frequency range is selected between 8 and 30 Hz. After performing both of them individually, they are combined to implement the proposed approach. For every subject, various time intervals are subjected to the three-feature extraction  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Left Hand**} & \multicolumn{2}{c}{**Right Hand**} & \multicolumn{2}{c}{**Feet**} & \multicolumn{2}{c}{**Tongue**} \\ \cline{2-10}
**Subject** & \begin{tabular}{c} **Frequency** \\ **Band (HZ)** \\ \end{tabular}  & \begin{tabular}{c} **Time** \\ **(Sec)** \\ \end{tabular}  & \begin{tabular}{c} **Frequency** \\ **Band (HZ)** \\ \end{tabular}  & \begin{tabular}{c} **Time** \\ **(Sec)** \\ \end{tabular}  & \begin{tabular}{c} **Frequency** \\ **Band (HZ)** \\ \end{tabular}  & \begin{tabular}{c} **Time** \\ **(Sec)** \\ \end{tabular}  & \ \begin{tabular}{c} **Frequency** \\ **Band (HZ)** \\ \end{tabular}  & \begin{tabular}{c} **Time** \\ **(Sec)** \\ \end{tabular}  \\ \hline
1 & 5–14 & 7.5–8.5 & 12–24 & 6.6–7.5 & 5–14 & 6–7 & 12–24 & 6.5–7.5 \\
2 & 5–14 & 7.5–8.5 & 12–24 & 7.5–8.5 & 12–24 & 6–7 & 5–14 & 5.5–6.5 \\
3 & 8–30 & 5.5–6.5 & 8–20 & 5.5–6.5 & 8–30 & 5.5–6.5 & 12–24 & 5–6 \\
4 & 8–30 & 6.5 technique, which are FFT and TDP, and then feature reduction technique, which is PCA. After this, the classification techniques, [PERSON]'s LDA and LS-SVM are performed. The number of frequency bands is calculated to identify which classification is optimum and which has a high accuracy. From the results, it is seen that the proposed combination of the feature extraction techniques works better when compared to its individual results. The frequency range for both the user-specific band and subject-specific band are identified and separated by applying the Discrete Fourier Transform (DFT) and Band Pass filter between the time interval. The analysis is statistically performed for both bands and the following points are selected as optimum, as shown in Table 2.  
In order to identify the best time interval, it has to be individually identified for all the subjects. This is compared with the different frequency intervals for both the bands. Additionally, LDA is also performed for the same frequency intervals in order to identify the optimum number of layers and epochs. The different traditional feature extraction methods, like TDP, PCA, R\({}^{2}\), FCBF, EMD, ITD, CV, and FFT, are compared with different combinations and tabulated in Table 3. The TDP and PCA are maintained as a constant technique, while modifying the other techniques--R\({}^{2}\), FCBF, EMD, ITD, CV, and FFT along with [PERSON]'s LDA as a classification. We are using the Fisher ratio of TDP. Therefore, TDP is constant. The highest average kappa coefficient accuracy obtained is 0.56 for the combination of TDP, FFT, and PCA. PCA is constant, because it is one of the most applied methods for feature reduction. From Table 3, it is observed that these combinations do not deliver high accuracy. The user specific band is performed with the combinations of TDP, FFT, and PCA with both LS-SVM and Fisher's LDA individually.