## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Subjects**} & \multicolumn{3}{c}{**A**} & \multicolumn{3}{c}{**B**} & \multicolumn{3}{c}{**Average**} \\ \cline{2-13}  & \multicolumn{4}{c}{**Repetitions**} & \multicolumn{4}{c}{**Repetitions**} & \multicolumn{4}{c}{**Repetitions**} \\ \cline{2-13}  & **1** & **5** & **10** & **15** & **1** & **5** & **10** & **15** & **1** & **5** & **10** & **15** \\ \hline \(App-1\) & 20 & 62 & 84 & 95 & 38 & 80 & 92 & 95 & 29.0 & 71.0 & 88.0 & 95.0 \\ \(App-2\) & 19 & 62 & 84 & 97 & 40 & 79 & 93 & 96 & 29.5 & 70.5 & 88.5 & 96.5 \\ \(App-3\) & 23 & 66 & 86 & 98 & 44 & 81 & 95 & 97 & 33.5 & 73.5 & 90.5 & 97.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Character recognition accuracy (%) for subjects “A” and “B” from BCI competition III dataset II using different approach in four repetitions. The ITR formula measures how efficiently a user can communicate with a computer using their brain signals, considering both speed and accuracy. This metric is typically expressed in bits per minute (bpm). The definition of ITR is as follows:  
\[ITR=((\log_{2}N+P\log_{2}P+(1-P)\log_{2}\frac{1-P}{N-1})\times 60)/T \tag{5}\]  
where \(N\) is the number of characters present in the BCI paradigm. In the present investigation, \(N\) has been determined to be 36. Additionally, the variable \(P\) represents character recognition accuracy, while the time taken for character recognition (\(T\)) has been explicitly defined as:  
\[T=2.5+((0.100s+0.075s)\times 12)\times Nr,1\leq Nr\leq 15 \tag{6}\]  
where \(Nr\) is the number of repetitions, which has been assigned values of 1, 5, 10, and 15 for this investigation. In Figure 4, a comparison is made between the Information Transfer Rate achieved by the proposed method and those reported in earlier studies. The results indicate that the proposed approach yields a higher ITR than the other techniques. Specifically, the proposed method exhibits the highest ITR at all repetitions, as evidenced by the values of 10.9 bpm, 13.7 bpm, 10.7 bpm, and 8.6 bpm for 1, 5, 10, and 15 repetitions, respectively. These findings suggest that the system's speed and character recognition accuracy are optimized across all repetitions. To utilize the proposed method on the BCI competition II dataset II, this study incorporated 42 training characters from subject C and 85 training characters from subject A to refine and optimize the model. On the 4 th repetition, the proposed technique accomplished a remarkable 100% accuracy rate on the BCI competition II dataset II. The character recognition accuracy for each epoch repetition is presented in Table 4 for reference. For this subject, we showed the effect of transfer learning on improving performance in a situation where all the training data  
Figure 3: It presents a t-SNE visualization showcasing the distribution of targets (30 characters of subject A) and sources (85 characters of subject B) from BCI competition III dataset II, both before and after transfer learning (TL) has been applied. The figure is split into two parts: (**a**) illustrates the distribution before TL, while (**b**) presents the distribution after TL. were used. While the reduction in calibration time was not directly proportional to the decrease in the number of training characters, our study has demonstrated higher character recognition accuracy compared with other state-of-the-art methods, as shown in Table 4.  
The proposed method exhibited a commendable 77.4% accuracy in character recognition after the second repetition, indicating its viability for real-time BCI spelling. Figure 4: Information transfer rate comparisons between proposed method and other methods reported in Table 3 based on the average accuracy of two subjects A and B from BCI competition III dataset II.