## 3 Results and Discussion
### The Results for the SSVEP-Based BCI with Multi-Task Learning  
The performance of the neural network using different domain features and MTL is examined in this section. The results of the proposed approach were compared with those using only a convolutional layer and a fully connected layer as the last layers of the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Hyperparameter** & **Value** \\ \hline Input layer & NS & 250 (1 s), 500 (2 s), \\  & & 750 (3 s), 1000 (4 s) \\ Convolutional block 1 & KS, FS, DL & T: 55/F: 53, 4, 1 \\ Dropout layer 1 & R & 0.5 \\ Convolutional block 2 & KS, FS, DL & 11, 4, 1 \\ Dropout layer 2 & R & 0.5 \\ Convolutional block 3 & KS, FS, DL & T: 35/F: 19, 8, 4 \\ Convolutional block 4 & KS, FS, DL & 35, 8, 4 \\ Dropout layer 3 & R & 0.5 \\ MTL block & KS & 250 (1 s), 500 (2 s), \\  & & 750 (3 s), 1000 (4 s) \\ Output layer & NS & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The hyperparameters of the proposed neural networks (NS: the number of nodes, KS: kernel size, FS: filter size, DL: dilation, T: for time-domain features, F: for frequency-domain features, R: dropout rate). neural network. The experimental results are presented in Tables 2 and 3 for using time and frequency domain features, respectively. The neural networks using a convolutional layer, fully connected layer, and multi-task learning as the last layer are denoted as C, FC, and MTL, respectively. The results show that the accuracy of the neural networks using time-domain features is higher compared to those using frequency-domain features. Hence, the time-domain feature is more suitable for neural network approaches. In addition, accuracy decreases when the duration of the SSVEP signal is decreased. The performance is too low for practical SSVEP-based BCIs when the input SSVEP signal duration is 1 s. While increasing the duration of the input SSVEP signal improves performance, it also increases the response time of the SSVEP-based BCI, which may be undesirable for users. Therefore, improving the performance of an SSVEP-based BCI with a short input signal duration would be very useful for practical applications. Tables 2 and 3 show that using MTL as the last layer in the neural network results in better performance than using the convolutional or fully connected layer. The accuracy can be significantly improved when the duration of the input SSVEP signal is 1 s. The improvement rates for using time- and frequency-domain features are 48.22% and 67.95%, respectively, compared to the convolutional layer. The improvement rates for using time- and frequency-domain features are 53.08% and 69.59%, respectively, compared to the fully connected layer. This demonstrates that using MTL as the last layer is effective at modeling the embedding features extracted from the 1-s SSVEP signal. Additionally, the response time of MTL with the 1-s duration is more practical for SSVEP-based BCIs. In Tables 2 and 3, the standard deviation is greater than 10%. To examine this further, the accuracy for each subject using a 4-s SSVEP signal is shown in Figure 2. The accuracy for subjects 11, 23, and 33 is below 80%, which greatly contributes to the higher standard deviation. This indicates that the SSVEP signals for some subjects cannot be effectively evoked. As a result, some individuals may not be able to communicate with others using SSVEP-based BCIs, which limits their practical application. This variability can be attributed to individual differences in neural responsiveness, fatigue, or visual discomfort. To address this issue, adaptive training strategies can be considered as a potential solution. Adaptive training involves tailoring the system to individual users by adjusting classifier parameters based on their specific SSVEP responses. By adapting the system to individual characteristics, the robustness and usability of SSVEP-based BCIs can be improved.