## 4 Experimental Studies
### Results
For the LS-SVM classifier, the obtained average kappa coefficient accuracy of all nine subjects is seen to be 0.57, whereas, for the [PERSON]'s LDA, the kappa coefficient accuracy is seen to be at 0.60. Currently, the FDA type F-score is performed with the combinations of TDP, FFT, and PCA with Fisher's LDA and the average kappa coefficient accuracy of 0.58 is obtained. The proposed technique is performed with the combination of TDP, FFT, and PCA, along with the user specific and FDA type F-score by using the Fisher's LDA classifier. With the proposed technique, it can be seen that the kappa coefficient accuracy is 0.64, which is significantly higher than the traditional approaches. This comparison is tabulated in Table 4 and represented in Figure 8. \begin{table}
\begin{tabular}{c c} \hline
**Subject** & **Time Interval** \\ \hline
1 & 6.5 \\
2 & 7.17 \\
3 & 5.5 \\
4 & 5.1 \\
5 & 6.6 \\
6 & 5.71 \\
7 & 6.12 \\
8 & 6.75 \\
9 & 5.95 \\ \hline \end{tabular}
\end{table} 
Table 2: Identified Optimum Intervals. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Methods** & **Classification** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **Avg.** \\ \hline TDP + R\({}^{2}\) + PCA & Fisher’s LDA & 0.69 & 0.19 & 0.60 & 0.39 & 0.19 & 0.36 & 0.76 & 0.69 & 0.67 & 0.50 \\ TDP + FCBF + PCA & Fisher’s LDA & 0.75 & 0.25 & 0.56 & 0.48 & 0.21 & 0.47 & 0.75 & 0.70 & 0.72 & 0.54 \\ TDP + EMD + PCA & Fisher’s LDA & 0.65 & 0.18 & 0.65 & 0.38 & 0.16 & 0.30 & 0.69 & 0.66 & 0.65 & 0.48 \\ TDP + ITD + PCA & [PERSON]’s LDA & 0.62 & 0.19 & 0.58 & 0.38 & 0.16 & 0.26 & 0.66 & 0.64 & 0.69 & 0.46 \\ TDP + CV + PCA & [PERSON]’s LDA & 0.72 & 0.27 & 0.55 & 0.49 & 0.25 & 0.48 & 0.71 & 0.73 & 0.76 & 0.55 \\ TDP + FFT + PCA & [PERSON]’s LDA & 0.74 & 0.26 & 0.57 & 0.48 & 0.27 & 0.49 & 0.70 & 0.75 & 0.78 & 0.56 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of standard feature extraction methods. Table 5 shows a comparison of the results of the proposed method and other competitive methods. The proposed technique of the user specific band is performed with combinations of TDP, FFT, and PCA (combined both Fisher ratio and FDA type F-score) with [PERSON]'s LDA, and the average kappa coefficient accuracy is seen to be at 0.64. The combination of methods, such as FFT, CV, and PCA, are performed with the LS-SVM classifier, and the kappa coefficient accuracy is seen to be at 0.56 [9]. The FBCSP algorithm were used to optimize subject-specific frequency bands for the CSP algorithm to extract features and the average kappa coefficient accuracy is seen to be at 0.63 [10]. The combination of CNN and VAE methods achieved the kappa coefficient accuracy of 0.56 [18]. The FBCSP algorithm that employed the MIRSR feature selection algorithm and yielded a kappa coefficient accuracy of 0.57 [31]. The Multiple discriminate analysis feature is performed with the SVM classifier and the kappa coefficient accuracy is seen to be at 0.55 [32]. The Autoregressive feature is performed with the LDA classifier, it can be seen that the kappa coefficient accuracy is 0.52 [33]. As a result, the proposed method offers a very satisfactory classification performance in comparison to the state-of-art methods. The obtained average result by the proposed method is 0.64, which is higher than 0.63 [10], and it is much higher than the other results that were obtained on BCI competition data [Table 5]. Our proposed approach outperforms the CNN method [17; 18]. Thus, the combination of methods such as TDP, FFT, and PCA to the frequency bands selected by Fisher ratio and FDA type F-score along with [PERSON]'s LDA yielded the most efficient results.