## 3 Results
### Classification Stage and Result Analysis
#### 3.5.3 Review and Comparison of Classification Result  
Compared with studies based on other similar BCI experiments, the classification accuracy obtained by proposed method matches state-of-art methods, with advantages in the data amount used for training and required preprocessing procedures, as shown in Table 3.  
In the BCI Competition IIb dataset published in 2003, [PERSON] used Continous Wavelet Transformation (CWT) for feature extraction and obtained 77% accuracy of binary classification [34]. In a similar brain-computer interface study of visual P300 ERP waveforms, [PERSON] et al. proposed Wavelet-Based Semblance Methods to Enhance the Single-Trial ERP detection, and reported 75% classification accuracy [35]. [PERSON] et al. proposed brain network connection as a classification feature; the accuracy rate obtained was about 80% [36]. Compared with these methods, the accuracy obtained by the proposed method is close to these methods, while they generally used ten or more channels. In these experimental paradigms of the visual P300 brain-computer interface, due to the long interval between visual stimuli, there is no overlap of ERP waveforms in the dataset used in this study, which had a negative impact on performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Method** & \multicolumn{10}{c}{**Subjects**} \\ \hline  & **VPnw** & **VPnw** & **VPnz** & **VPoa** & **VPob** & **VPoc** & **VPod** & **VPja** & **VPoe** & **Avg** \\ \hline Proposed & 78.00\% & 75.90\% & 75.10\% & 73.60\% & 79\% & 79.50\% & 71.20\% & 81.00\% & 74.50\% & 76.42\% \\ Original & 77.0\% & 75.0\% & 74.4\% & 72.2\% & 78.6\% & 79.6\% & 70.0\% & 82.0\% & 73.2\% & 75.78\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Binary Classification Accuracy Comparision.  
In the auditory ERP brain-computer interface research, [PERSON] et al. used single-channel EEG data and the traditional Step-Wise Linear Discriminant Analysis(SWLDA) method to obtain 79% accuracy [37]. [PERSON] et al. conducted in-depth analysis and believed that it is difficult to improve the accuracy of binary classifications in the auditory brain-computer interface [40]. Therefore, the significance of the proposed method lies in the fact that fewer data are needed for classification, which is very important in wearable brain-computer interface devices. When comparing with other methods, it should be noted that, in addition to accuracy, the proposed method does not require common preprocessing operations such as artifact removal and filtering. It only requires down-sampling and normalization operations. Compared with other methods, it reduces computation effort, since artifact rejection is often time-consuming and requires heavy computation. The main reason for this is that the KSVD modeling process will put the artifact interference into separate dictionary atoms.  
In addition to traditional feature extraction and classification methods, deep learning methods, especially the convolutional neural network (CNN) [41], have already been applied to BCI applications. [PERSON] et al. introduced CNN to Visual ERP BCI and achieved higher accuracy [38]. Then, [PERSON] found that with a large dataset, CNN can provide a comparable performance in P300 ERP-based BCI with zero training [39]. Compared with the method in this paper, a higher amount of data is still needed for training in the CNN-based method. Besides, [PERSON] et al. proposed that the deep-learning method has obvious vulnerabilities and can be attacked by adversarial examples [42]. This makes deep-learning methods unstable in practical applications.  
To summary, compared to common methods, the proposed method obtained comparable classification results, with a smaller amount of data and calculation required.