## 1 Introduction
### Common EEG Feature Extraction Methods for ERP Classification
developed a personal identification system using a sparse-modeling-based EEG signal compression-sensing method [16]. Because of the application of sparse modeling and compressed sensing, the amount of data transmission during the operation of the system can be reduced, so that the system can use low-cost wearable EEG acquisition equipment and run on the World Wide Web, which is convenient for application. [PERSON] et al. used Regularized Group Sparse Discriminant Analysis to identify the EEG signal in the brain-computer interface paradigm and identify the P300 waveform in the EEG [17]. [PERSON] et al. directly used sparse representation coefficients as classification features to perform classification in Motor Imagery BCI [18]. [PERSON] et al. added the incoherence measure to the sparse dictionary update process and used this dictionary to sparsely decompose the EEG signal to obtain better classification features [19]. [PERSON] et al. used kernel sparse representation to sparsely reconstruct EEG data and used sparse reconstruction coefficients as classification features to identify EEG data holding epileptic components [20]. [PERSON] et al. use sparse representation to decompose EEG data for Visual Evoked Potential (VEP) extraction [21]. [PERSON] et al. applied sparse representation to the BCI system of motion imagination and used the Gabor base to construct a dictionary to extract recognizable waveforms from EEG [22]. However, because the definite basis is used, the waveform components are difficult to make accurate and it is impossible to express the nature of the signal. However, due to the over-completeness of the sparse dictionary, the ERP waveforms in the EEG signal will be distributed among multiple sparse dictionary atoms, which makes it difficult for the sparse reconstruction coefficients to become stable classification features. From the perspective of sparse decomposition theory, since these dictionary atoms are used to perform sparse reconstruction of EEG signals with low errors, these atoms must hold the information needed to identify ERP. Therefore, an extra approach is required to fully utilize ERP waveform information contained in the sparse dictionary atoms for classification features from EEG signals for ERP recognition. Self-organizing mapping (SOM) is an appropriate approach to solving the problem of scattered ERP in atoms. SOM is a method to produce a typically two-dimensional representation of the input space of the training samples. When the training samples are sparse dictionary atoms, SOM can combine the scattered ERP information into code vectors of the SOM network. SOM was first proposed by Professor [PERSON] of the University of Helsinki in Finland in 1981 [23; 24]. [PERSON] believes that when a neural network accepts external input patterns, it will be divided into different corresponding areas, with each area having different response characteristics to the input mode, and this process is completed automatically. SOM is already a common method in the field of biomedical signal analysis and is widely used in the analysis of neural activity data. [PERSON] et al. used SOM to analyze the time-domain activity waveform of each voxel in Functional magnetic resonance imaging (fMRI) and aggregated the neuron nodes in SOM according to the correlation to find a pattern of voxel activity [25]. [PERSON] et al. used SOM to perform hierarchical cluster analysis of spatio-temporal features on fMRI image data to find fMRI classification features that can represent cognitive activities [26]. [PERSON] et al. used SOM to perform cluster analysis on EEG signals collected in the clinical scenarios and classify EEG fragments containing epileptic electrical activity and normal EEG fragments [27]. [PERSON] et al. first extracted features from EEG signals, and then analyzed the features using SOM to recognize human emotions from EEG [28]. [PERSON] et al. used SOM to extract features from EEG for a BCI system that can recognize human cognitive states [29].