## 4 Results and Discussion
### SSVEP Detection Results  
Results are carried on five healthy and young subjects (21-25 y/o) with normal or corrected-to-normal vision. For each subject, we collect 20 3-s EEG signals under flickering and no flicking conditions each, and then compute the \(RC\) values of the signals. Table 2 lists the average \begin{table}
\begin{tabular}{c c c c c} \hline
**Subject** & \(\boldsymbol{d}\) & \(C\) & \(\boldsymbol{\sigma_{k}}\) & \(\boldsymbol{\sigma_{s}}\) \\ \hline
1 & 3 & 100 & \(2^{4}\) & \(2^{11}\) \\
2 & 9 & 100 & \(2^{7}\) & \(2^{13}\) \\
3 & 5 & 100 & \(2^{7}\) & \(2^{8}\) \\
4 & 4 & 1000 & \(2^{2}\) & \(2^{11}\) \\
5 & 7 & 100 & \(2^{8}\) & \(2^{12}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Optimal P300 detector parameters for each subject. \(RC\) values over the five subjects. It can be observed from this table that the \(RC\) values of LED flickering condition are much higher than those of no flickering conditions for every EEG recording channel. Moreover, among the three different channels, the largest \(RC\) difference between the two conditions is at Oz. Therefore, it is easy to distinguish SSVEP from non-SSVEP in EEG signals by using \(RC\) as the feature and setting an appropriate threshold \(T\). In the following experiments, we set \(T\) = 0.6. Table 3 lists the true positive rate (TPR) and false positive rate (FPR) of the SSVEP detection results at Oz, where TPR refers to the fraction where SSVEPs are classified as the SSVEP class, and the FPR is the fraction where non-SSVEP EEG signals are classified as the SSVEP class. The higher the TPR is, the higher the SSVEP detection accuracy is. According to Table 3, we can see that our BCI system can achieve high SSVEP detection accuracy. For example, both subject 1 and subject 4 achieve a TPR of 100%, and the average TPR is 93%. Moreover, the results in Table 3 are based on the best combinations of flickering frequency and duty cycle listed in Table 4, from which we can observe that the best combinations of the two parameters for the five subjects are different. On the other hand, the higher the FPR is, the more unstable the switching mode is. If the FPR is high, the healthcare function selection mode is easy to activate even if the subject has no intention to open this mode (_i.e._, even the subject is not gazing at the flickering LED). According to Table 3, the proposed BCI system achieves a low average FPR of 14%. Among the five subjects, the FPR of subject 2 is the lowest, only 5%. However, the FPRs of both subjects 3 and 4 are relatively high (20%). The plot of the average \(RC\) values of the five subjects under flickering and no-flickering conditions is shown in Figure 6. We can observe from this figure that for subject 3 the \(RC\) distributions of the two conditions have a large overlap. Nevertheless, the FPR can be further decreased by setting the threshold to a value higher than 0.6. Therefore, when the switching mode is performed online, the threshold could be defined by the user a prior. The experimental results have shown that the simple SSVEP detection strategy adopted in this paper can provide the switching mode with sensitive and stable performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Best Combination** & **Sub1** & **Sub2** & **Sub3** & **Sub4** & **Sub5** \\ \hline Flickering Frequency (Hz) & 22.72 & 24.39 & 23.26 & 24.39 & 21.74 \\ Duty Cycle (\%) & 70 & 70 & 30 & 10 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Best combination of flickering frequency and duty cycle for each subject. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Channel**} & \multicolumn{2}{c}{**Flickering**} & \multicolumn{2}{c}{**No Flickering**} \\ \cline{2-5}  & **Average \(RC\)** & \(Sd\)** & **Average \(RC\)** & \(Sd\) \\ \hline O1 & 1.55 & 0.61 & \(-\)0.22 & 0.34 \\ Oz & 1.88 & 0.67 & \(-\)0.16 & 0.48 \\ O2 & 1.42 & 0.58 & \(-\)0.27 & 0.41 \\ \hline \hline \end{tabular}
\end{table}