# Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI
## 2 Previous Work
A person with three channels was used for the experiment. Results between 68% and 84% were obtained for different classifiers. [PERSON] [44] used the method of the Warp Laser Space Group. The feature extraction is based on the statistical time domain, spectral power, autoregression, and wavelet coefficients in their study. The Warp Laser space group with strategy autoregression model has been used to select channels and features. The total results of the dataset were 83.37%, which reduced the number of channels to 17 and 18 channels, resulting in an accuracy of 84.7%. [PERSON] [45] used noise elimination cases, cross-correlation normalization by selecting effective channels (from the left and right nipples), and the calculation of data statistics along with ANN and SVM classifiers. Data are collected by a sensory headset device to imagine left and right finger movements. With 14 channels and 420 features, they obtained an average accuracy of 95%. With 10 features and 14 channels, they obtained an average accuracy of 96.69%. With three features and 14 channels, they obtained an average accuracy of 97.34%. In the article by [PERSON] [46], where the sensing headset collected the data, the feature selection method was used in addition to the SVM, ANN, and NB classifiers. With 14 channels and 11 features selected for three classes and one person for NB, the accuracy was 82.97% for ANN 83.07% and 83.26% for SVM. [PERSON] [47] used the channel analysis method to select channels for filtering banks. Three to 14 channels were selected for filtering banks, and feature extraction was done by CSP. Channel reduction was made based on the accuracy of its valid crossover, and the feature reduction was not performed. The total average between 13 and 14 channels is 84.51%, and for three channels, it is 75%. [PERSON] et al. [48] used feature selection from the filter bank common spatial pattern (FBCSP) method with the NBPW classifier. With the original filter bank of CSP (oFBCSP), kappa was 60.7%. With the S filter bank of CSP (sFBCSP), kappa was 61.9%. With the E filter bank of CSP (eFBCSP), kappa was 63.5%. [PERSON] et al. [49] applied the FDCSP frequency amplitude method. The accuracy results obtained for CSP, FDCSP, SCSP, TRCSP, WTRCSP, and FBCSP were 78%, 82.94%, 81.63%, 78.79%, 78.47%, and 79.30% respectively. Finally, [PERSON] et al. [50] used the neural network model to extract features with five layers of CNN, with different classifiers including Power + SVM, CSP + SVM, and Power + SVM. They achieved accuracy of 81.25%, 82.61%, and 77.17%, respectively, for each classifier, and the average of the proposed method for the two subjects was 86.41%. We summarize the advantage and disadvantages of the previous methods that we presented above:  
Advantages:  
* Most of them use heuristic algorithms, which achieved good performance related to their works. * The heuristic algorithms are suitable for selecting features with or without extracting features using conventional methods. * In all of them, two general models are used that include (1) feature selection on preprocessed data before the final classification, (2) feature selection after feature extraction on preprocessed data followed by the final classification. It has been demonstrated that the second method is more efficient. * Most of them used continuous frequency domain for processing. * All of the previous works only used conventional algorithms for the EEG area. Disadvantages:
* The heuristic algorithms fail to converge very fast. * All of them only used a large frequency domain (8 to 30 Hz) that is not effective enough for feature extraction. * Even though the conventional algorithms are well established, there is still a need for new methods with higher performance and accuracy for processing the EEG data. * A combination of two general models is not studied. * Smaller continuous domains reduce the noise and distinctive features between classes and, as a result, reduce the accuracy.