## 1 Introduction  
Brain-computer interfaces (BCI) can provide a direct communication method between the brain and a computer or other external devices [1; 2; 3]. There are several types of electroencephalograms (EEG) signals used in BCI, such as P300 potential [4], steady state visual evoked potential (SSVEP) [5], motor imagery (MI) [6], and so on. Specifically, P300-based BCI is one of the most common BCI systems, as the P300 potential is easy to be stimulated. Compared with other signals, the P300-based BCI system has some advantages: (1) P300 signal is extremely easy to measure and non-invasive; (2) less training time; (3) suitable for most subjects, including those with severe neurological diseases; and (4) users only need to provide a simple control signal [7]. It can implement a variety of different functions, and can even be used in the home of people with disability [7; 8]. [PERSON] and [PERSON] of the United States introduced the first P300-based character input system in 1988 [9; 10], which has been applied until now. The system contained a 6*6 matrix of visual stimulation interface, which was composed of English letters, numbers, and spaces. Before the experiment, the subjects were told that a specified character in the visual stimulator was the target character, and each experiment randomly assigned a character. During the experiment, the subjects were asked to keep an eye on the target character position in the visual stimulator, while any row or column in the visual stimulator flashed randomly. When the target character's row or column was flashing, a positive potential (called P300 ERP) related to the event could be detected in the subject's scalp (about 300 ms after receiving the stimulus); if not, the detected EEG data were non-P300 event-related potentials (N-P300 ERP) [11]. In addition to this standard speller system, there are other paradigms, such as row-column (RC) paradigm [12], single character (SC) paradigm [13], region-based(RB) paradigm [14], and so on. For all these systems, how to identify quickly and accurately is critical to improving the performance of BCI systems. Due to the collected P300 signals often being high dimensional and feature dependent, some methods were proposed to enhance the feature extraction. PCA (Principal Component Analysis), a principal component analysis method, is widely used in feature extraction and data dimensionality reduction. The principle of PCA is to transform the original signal matrix into a covariance matrix [15] through linear transformation [16], and obtain a new signal matrix by filtering the eigenvalues and eigenvectors of the matrix [17]. The new signal matrix retains some of the most important original signal features in the original signals matrix, and eliminates noise and unimportant features to achieve the purpose of dimensionality reduction. In recent years, many researchers have applied the PCA to reduce the dimensionality of the obtained EEG signals. [PERSON] used different dimensionality reduction algorithms to process the EEG signals for the dimensionality reduction part of the data, and found that the use of PCA for dimensionality reduction of P300 signals performed best, compared to independent component analysis (ICA) and linear discriminant analysis (LDA) [18]. [PERSON] and [PERSON] used PCA to reduce the dimensionality of P300 signals, and then used SVM to classify the reduced-dimensional signals. PCA reduced the computational burden of weighted classifiers and speeds up the classification speed [19]. Like combined multi-scale filters and PCA to classify EEG signals, the classification accuracy can reach 91.13% [20]. PCA fits in a brain-computer interface, especially the P300 brain-computer interface. Previous works on P300 classification mainly employed traditional machine learning algorithms, such as support vector machine (SVM), linear discriminant analysis (LDA), and so on.