## 4 Results and Discussion
### Comparison with Other Works  
The work of [35] develops a P300 and SSVEP-based BCI speller. Their method detects the target item in the spelling matrix by combining two kinds of stimulation paradigms, including the conventional row/column (\(RC\)) P300 and a two-step SSVEP stimulation paradigms. The \(RC\) P300 paradigm used in their system follows the oddball paradigm used in [24], _i.e._, the rows and columns of the stimulation matrix flashed in a random order, one at a time, which is the same as the P300 paradigm used in the function selection mode of our proposed BCI. As for their two-step SSVEP paradigm, all items in the same row flicker at the same frequency in step 1, and those in the same column subsequently flicker at the same frequency in step 2. Finally, the target item in this matrix is estimated by using a maximum-probability estimation (MPE) method to fuse the scores obtained from the \(RC\) P300 and the two-step SSVEP paradigms. Therefore, the hybrid BCI of [35] utilizes the P300 \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**P300 Detectors** & \(N\) = 1 & \(N\) = 2 & \(N\) = 3 & \(N\) = 4 & \(N\) = 5 & \(N\) = 6 & \(N\) = 7 & \(N\) = 8 & \(N\) = 9 & \(N\) = 10 \\ \hline \(K\)-NN & 63.2 & 66.1 & 68.3 & 75.0 & 78.1 & 81.7 & 85.0 & 85.9 & 88.2 & 89.1 \\ SVM & 71.7 & 72.5 & 76.4 & 80.2 & 84.1 & 86.4 & 88.1 & 90.1 & 92.3 & 93.1 \\ FLDA & 64.1 & 65.8 & 69.7 & 75.3 & 77.8 & 82.7 & 85.4 & 87.2 & 88.7 & 90.3 \\ BLDA & 68.5 & 72.4 & 77.2 & 80.3 & 83.6 & 86.4 & 87.7 & 88.9 & 90.5 & 92.8 \\ Gradient boosting with OLS & 72.5 & 76.3 & 81.7 & 82.3 & 83.5 & 86.3 & 89.3 & 93.1 & **95.6** & **95.6** \\ Kernel FDA + SVM & **76.4** & **81.3** & **83.7** & **86.4** & **88.1** & **89.5** & **91.0** & **93.2** & 95.4 & 95.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparisons of average classification accuracy between different P300 detectors (in %). \(K\)-NN: \(K\)-neighbor neighbor; SVM: support vector machine; FLDA: Fisher linear discriminant analysis; BLDA: Bayesian linear discriminant analysis; OLS: ordinary least squares; Kernel FDA: Kernel Fisher’s discriminant analysis. and SSVEP fusion method to gain better target item detection accuracy than a pure P300-based BCI, which is the same as the objective of the hybrid BCI in [20] where both P300 and SSVEP paradigms are performed simultaneously to get better detection accuracy than a pure P300-based BCI. Therefore, the SSVEP and P300-based BCIs proposed in [20; 35] are based on a parallel architecture, which is different from the proposed BCI. Our SSVEP and P300-based BCI system is based on a cascade architecture, which is composed of two modes; one is the switching mode and the other is the function selection mode. The function selection mode is initiated only when SSVEP is detected in the switching mode. As soon as the function selection mode is turned on, our designed \(5\times 5\) healthcare function selection panel matrix starts to flash following the conventional \(RC\) P300 paradigm. After the P300 paradigm is over, the proposed P300 detector kernel FDA + SVM determines the target function (_i.e._, target item) in the healthcare control panel matrix. In other words, although the proposed BCI is also based on SSVEP and P300, the SSVEP is not used for target item detection, but is used for asynchronous control of the function selection mode. Also, by the design of the SSVEP-based switching mode, the users can turn on the function selection mode in a self-paced manner, increasing the usability of the BCI in practical use. In the future, the SSVEP stimulation paradigm used in [20; 35] should be introduced to our function selection mode to enhance the accuracy of target function detection. [PERSON] _et al._[36] presented a hybrid human-machine interface (HMI) based on two different type of physiological signals, including EEG and electrooculography (EOG). Their hybrid HMI consists of two modes, EOG mode and EEG mode.