# Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI
## 2 Previous Work  
In the article [10], [PERSON] et al. implemented specific individual and specific masks working with the CAR method, standard resource methods, and ELM. The method was a two-dimensional reduction of features and electrodes, which has achieved accuracy in the range of 15 to 32%. In the article [33], [PERSON] et al. performed the feature extraction method by analyzing the wavelet components for two channels (each channel separately) in two modes, first with a dynamic frequency feature selection method and second with a full frequency range (without frequency range selection). They obtained accuracy results of 68% and 67% for the two modes, respectively. So, the feature selection had improved their accuracy. In their work, feature selection was performed after feature extraction, which reduced the channels from 22 to two channels. [PERSON] et al. [34] used the glow algorithm to select the features. First, feature extraction is performed by CSP for all channels and three important and specific channels individually (channels are left hemisphere, right hemisphere, and center of the brain). The GA, PSO, and a Firefly Algorithm obtained 59.85%, 60%, and 70.20% accuracies, respectively. In the article [35], [PERSON] used the sixth-order autoregression method with a sample window shift. They reduced the features to 20 features (characteristics) with PCA and then classified them with LDA. They obtained accuracy results between 46.8% and 59% for one channel and between 48% and 62% for two channels. They reported the average of the best accuracies in different non-identical channels as 59.67%. In the article [36], [PERSON] et al. designed an individual and specific work mask that has been shared between individuals, so that part of the data is trained for subjects and tested for the untrained subjects. Features reduction and channel reduction were up to 90%, and the average accuracy was between 73.5% and 74.5%. Inthe article [37], [PERSON] et al. used frequency-temporal and temporal-frequency feature extraction. They implemented five channels with ACO, DE, GA, SA, and PSO algorithms along with the PNN classifier to select the features. The best result in this study was 65% for four classes. [PERSON] [38] used the MNE and Infomax preprocessing methods along with feature extraction with spectral power by selecting the feature by GA and SVM classifier. In this paper, for two classes, the number of channels has been reduced from 32 to two. The accuracy range of the subject is between 55% and 67%. By changing the method of the CSP with SVM classifier, [PERSON] [39] obtained 70.90% accuracy for three channels, 79.07% for four to 14 channels, and 81.63% accuracy for nine to 19 channels, while the total accuracy for the standard CSP is 79.23%. [PERSON] et al. [40] used the time domain parameter method to extract the feature and the time-space optimizer to select the optimal channels along with the Fischer analysis classifier. When three channels were selected from 118 channels with BP and TDPS, average results of 71% and 72% were obtained, respectively. When using all the channels, the accuracy was 76%. While in their proposed model, they could increase the accuracy up to 78%. [PERSON] et al. [41] used the method of extracting features from nine filter banks (bands) using the CSP for each filter bank. After feature selection from features collected from all the filter banks, the NBW classifier is used for classification. In their model, the reduction of channels was three or 13 out of 22 channels. The average accuracy was 75% for three specific channels and 87% for 13 channels. In the article [42] by [PERSON], the BSS and PCA methods were used to select the feature. Three channels and 12 filter banks (bands) were used. However, in this study, only one person was examined. For different modes, the accuracy was between 55% and 80% for FSS and between 52% and 87% for PCA. [PERSON] et al. [43] have implemented a modified feature extraction method with wavelet subbands and feature selection by GA with four classifiers.