## 3 Results and Discussion
### The Results for the SSVEP-Based BCI with Multi-Domain Features  
The SSVEP-based BCI using multi-domain features is examined in this section. Typically, the concatenation operation is used to combine multi-domain features, but it increases the model size and computational complexity of the neural network. To reduce this complexity, the concatenation operation was compared with the element-wise addition operation. Moreover, to investigate the impact of the feature learning layer (Convolutional Block 3 in Figure 1), we designed two different neural network architectures for this experiment. In MDF1, the feature learning layer (Convolutional Block 3) is positioned before the fusion operation, which involves concatenation or element-wise addition. In contrast, MDF2 modifies this structure by removing the two feature learning layers from Convolutional Block 3 and introducing a new feature learning layer after the fusion step. This adjustment allows feature learning to take place after the integration of multi-domain features.  
The experimental results of MDF1 and MDF2 are shown in Table 4. The performance of the neural network using element-wise addition as the fusion operation is slightly better than when using the concatenation operation. Additionally, the number of parameters in the neural network using element-wise addition is lower than that in the neural network using concatenation. Therefore, the element-wise addition operation is more suitable for combining multi-domain features.  
In Table 5, the performance of MDF2 is slightly better than that of MDF1. For MDF2, the embedding features are learned independently in the time and frequency domains, and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **FO** & **4 s** & **3 s** & **2 s** & **1 s** \\ \hline \multirow{3}{*}{MDF1} & Con & 94.8 & 93.7 & 91.6 & 85.4 \\  & Add & 95.0 & 94.3 & 92.6 & 85.5 \\ \hline \multirow{3}{*}{MDF2} & Con & 95.0 & 94.1 & 92.0 & 85.0 \\  & Add & 95.1 & 94.4 & 92.7 & 85.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The accuracy of MDF1 and MDF2 using concatenation or addition operations (FO: fusion operation, Con: concatenation operation, Add: addition operation).  
Figure 2: The accuracy of the SSVEP-BCI for each subject using 4 s SSVEP signals. The convolutional block and fully connected block were selected for comparison with the multi-task learning block. The results show that the SSVEP signals for some subjects may not be effectively evoked.  
then fused using the element-wise addition operation. The convolutional layer processes the embedding feature, making it more meaningful to represent the characteristics of each domain.  
Comparing the results in Tables 2 and 3, the performances of MDF1 and MDF2 are higher than that of a neural network with MTL that uses either time- or frequency-domain features. Table 5 provides a detailed analysis of the improvement rates, showing that fusing different domain features can effectively enhance the performance of SSVEP-based BCIs. Moreover, the minimum improvement rate of the SSVEP-based BCIs when using input SSVEP signal durations of 4 s, 3 s, 2 s, and 1 s, are 24.2%, 40.6%, 43.5%, and 9.4%, and 31.5%, 26.9%, 31.5%, and 35.3% for the use of time- and frequency-domain features, respectively. Therefore, fusing different domain features is an effective approach for improving the performance of SSVEP-based BCIs.