## 3 Results  
Initially, to demonstrate the effects of our suggested technique and represent the data distributions, we employed t-Stochastic Neighbor Embedding (t-SNE) as a method to transform the high dimension data into a lower dimension space [33]. In Figure 3a, we plotted the filtered EEG signal of the source and target subjects, which consisted of 85 characters from subject B and 30 characters from subject A, respectively. Blue and red dots represented each trial in the target and source data, respectively. On the other hand, Figure 3b highlights the success of our proposed hybrid transfer learning method by presenting the distribution of features extracted from the data. The blue and red dots in the plot indicated the target and source trials, respectively. This visualization exemplified the ability of our suggested transfer learning technique to align the data distributions between target and source subjects, which is crucial for successful transfer learning. It is apparent that the source and target distributions are similar to each other after EA. To assess the efficacy of our proposed method in line with reducing calibration time, we conducted several testing sessions using a widely recognized dataset. We evaluated the performance of the proposed approach for various repetitions, including 1, 5, 10, and 15. The results of these sessions are presented in Table 2, which compares the performance of three different training approaches for two subjects. The study employed three distinct approaches to investigate the efficacy of transfer learning techniques for character recognition. The first approach, \(App-1\), followed a conventional method and did not integrate transfer learning. This approach relied on 30 target training characters and 100 test characters. The second approach, \(App-2\), utilized a transfer learning technique based on fine-tuning in a CNN but did not incorporate the EA technique. \(App-2\) employed 30 target training characters, 85 source characters, and 100 test characters. Finally, in \(App-3\), the study employed a fine-tuning approach in a CNN and incorporated the EA technique. This approach also used 30 target training characters, 85 source characters, and 100 test characters. The findings indicate that the approach of \(App-3\) outperforms other methods in character recognition accuracy. Additionally, this approach achieves high accuracy using fewer samples, making it well-suited for real-time implementation. By utilizing only 30 out of the 85 available training characters for target subjects, we have managed to save about 29 min (\(55\times 180\times 0.175=1732.5\) s) in calibration time. In order to highlight the effectiveness of our suggested approach (\(App-3\)), we have conducted a comparative analysis of the character recognition outcomes with respect to prior research utilizing the most advanced dataset, as presented in Table 3. Our scheme demonstrates that achieving a strong performance while using fewer training samples requires a balance between accuracy and trade-offs compared with other methodologies. Information transfer rate ( ITR) is frequently used to assess the efficiency of a brain-computer interface system. This metric gauges the speed at which data can be transmitted between the brain and an external device, such as a computer. Measured in bits per second, ITR is a critical indicator of the BCI system's performance. A higher ITR can significantly enhance the speed and accuracy of communication between the brain and external devices, making it a crucial factor in determining the system's effectiveness.