## 3 Results  
The individual classification accuracies of the 12 subjects within different TWs for CCA and L1-MCCA are shown in Figure 3. This figure shows that when the TW increased, the accuracies of the CCA and the L1-MCCA methods both improved. Additionally, the accuracies of the L1-MCCA were higher than those of the CCA. Figure 3: The SSVEP recognition accuracy rates of the 12 subjects derived by the CCA and L1-MCCA, respectively, with different time window (TW) from \(1\) to \(6\) s.  
The correlation coefficients corresponding to diverse reference signal frequencies derived from the CCA and L1-MCCA for a typical subject (S1) are shown in Figure 4. The classification performance was evaluated by comparing the correlation coefficient values of the two methods under different reference frequencies. Compared with CCA, L1-MCCA obtains the correlation coefficients corresponding to different reference signal frequencies (6.8, 12.5, 12, 9.7, 11.1, 8.4, 10, 8, and 14.7 Hz) through leave-one-out cross-validation. It can be seen from Figure 4 that the correlation coefficient between the target frequency and the corresponding reference signal is much higher than those corresponding to other frequencies using both methods. At each frequency, the greater the difference between the highest value and the other values, the more distinct the classification effect. The comparison of the two techniques shows that L1-MCCA has a better effect, and the classification accuracy based on L1-MCCA is predicted to be higher than that of CCA. To test the feasibility and evaluate the performance of the method proposed in this study, disparate techniques were compared quantitatively in terms of classification accuracy. Figure 4: Correlation coefficients corresponding to different reference signal frequencies, obtained from the leave-one-out cross-validation of S1 with time window (TW) length of 6 s by the CCA and L1-MCCA, respectively. Error bar represents the standard error of the correlation coefficient. The comparison results of the classification accuracies of dissimilar methods with different TWs are shown in Figure 5.  
As shown in Figure 5, from the perspective of each method alone, the longer the TW, the more experimental information obtained, and the higher the corresponding classification accuracy. For the PSD method, the accuracy of 1 s TW was 37.58% and was increased to 39.70% after optimization. The accuracy of 2 s TW was significantly improved to 70.91% (\(p<0.05\)), and further improved to 73.03% after optimization. When the TW was longer than 3 s, the classification accuracies were maintained stably, and the optimized ways were bigger than those of the unoptimized method. For the CCA method, the optimization effect PSO-SVM was significantly larger than the PSD method. Specifically, the accuracy of 1 s TW was significantly improved from 31.91% to 52.42% (\(p<0.05\)) after optimization. The accuracy of 2 s TW was significantly increased to 77.28% (\(p<0.05\)) and 94.85% after optimization (\(p<0.05\)). In addition, for the TWs that are longer than 3 s, the classification accuracies of the PSO-SVM optimized method were significantly larger than the unoptimized CCA method. Specifically, the accuracies were improved from 88.15% to 96.67% (3 s TW, \(p<0.05\)), from 88.58% to 95.76% (4 s TW, \(p<0.05\)), from 87.41% to 95.96% (5 s TW, \(p<0.05\)), and from 87.72% to 96.36% (6 s TW, \(p<0.05\)), respectively. For the L1-MCCA method, the optimization effect PSO-SVM was more distinct. The accuracy rate was significantly improved from 40.62% to 65.76% (\(p<0.05\)) for 1 s TW. The accuracy of 2 s TW was significantly (\(p<0.05\)) improved to 80.62% before optimization. After optimization, it was further significantly increased to 96.36% (\(p<0.05\)). After that, the classification accuracies for the TW longer than 3 s were stable, but the accuracies after PSO-SVM optimization were significantly higher than before.