## 4 Discussion  
The current mainstream EEG feature extraction methods for ERP-based BCI usually require multi-channel data and have difficulties in processing non-stationary waveform. Sparse modelling-based methods provide certain improvements, but the sparse features are often unstable and challenging to interpret. Therefore, there is the question of how to extract better features to overcome those drawbacks. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} Only simple procedures. \\ Huge computation effort. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: State-of-art Study Comparision. In order to answer this question, we proposed a novel EEG signal feature extraction method for event-related potential recognition. In this method, we first performed sparse dictionary-learning on the EEG signals of the training set to obtain the sparse dictionary, and then performed self-organize mapping on the dictionary atoms, and used the cosine similarity between the sample to be recognized and the code vectors of the SOM network as the classification feature. The results in Figures 9 and 10 showed that the code vectors in the SOM network trained by sparse dictionary atoms were highly similar to the ERP waveforms obtained by multiple superpositions. This means that the proposed method successfully processed non-stationary ERP waveform. As shown in Table 2, the proposed method could provide stable and useful classification features from single-channel data. The results proved that the proposed method is the answer to the question above. The classification result was comparable to other studies, as presented in Table 3. Compared with traditional and most state-of-art ERP extraction methods, this method achieved a similar classification accuracy result with fewer data required for it to work and less computation effort. This method has three strengths. First, based on the results in Figures 9 and 10, the features extracted by the method in this article use sparse dictionary atoms as a bridge to establish a connection between the EEG frames to be recognized and the target ERP waveform. This gives the extracted features a certain electrophysiological meaning. Second, proposed method only requires single-channel data, and ERP can be extracted in a single trial. Third, this method requires fewer preprocessing steps than traditional methods, especially as the artifact rejection is not required in the proposed method. Based on the above strengths, the proposed method is most suitable for wearable brain-computer interface applications. In this application scenario, the energy efficiency of data transmission is low, and the data analysis and computation capabilities of the device itself are limited, so the data must be transmitted to the remote server for complex analysis. When using the method in this paper, since the SOM network has been trained, only a few cosine distances need to be calculated locally to perform classification. On the other hand, when the remote server and the wearable brain-computer interface device share a sparse dictionary, only sparse data need to be transmitted to recover the collected EEG waveforms on the remote server for further and more complex analysis tasks. The proposed methods have limitations and can be further improved on in the future, mainly in the following aspects: First, in the proposed method, the Best Matching Unit (BMU) is selected by Euclidean distance. Other distance measurements may provide better results. Second, the cosine similarity in this article is used as a classification feature. If we use other calculation methods to determine the time series relationship, we may get features more adaptive to the nature of the samples. In summary, in this article, we proposed an EEG feature-extraction method for BCI applications. The classification results on the public dataset show that this method requires fewer preprocessing steps and a smaller amount of data than traditional methods and other state-of-art studies.