# Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI
## 3 Material and Methods
### Reduce and Extract Features of Brain Signals with Filter Banks and Bands (FBs)  
In some studies, feature and electrode reduction methods have been used in combination or separately to process information to reduce data volume. GA and PSO or some other algorithms have been implemented for each person or individual to reduce the dimensions before extracting the features or selecting the features after extracting the features or as the classifiers for classification separately. In this paper, our proposed method is inspired by different methods to reduce the dimensions, while the proposed method is somewhat different from the methods presented in some articles. In a way, it has partial similarities with the items mentioned in the previous. First, in all our models, bandwidthreduction (filter bank) has been used to extract the feature. Second, in some cases, the reduction of features and electrodes has also been used. Third, selecting the feature is done after extracting the features. For this purpose, we implemented different models, which support all items. These models (five general models) are as follows:  
1. The selection of features is made through a filter bank with a specific channel. Two filter banks and two channels are used for the purpose (separately for individuals by selecting two filters and two specific channels). 2. The selection of features is made through a hybrid filter bank with a specific channel, and a new signal from two filter banks along the channel is used for this purpose. (Separately for a person with a combined signal and two specific channels). 3. Reduction of features for two channels with two filter banks (in the range of 8 Hz) is intended for frequency reduction. We have a 63% reduction in the frequency range; i.e., from the whole frequency domain, which is 8 to 30 Hz, we have used a combination of smaller frequency ranges. For example, 8 to 12 Hz and 20 to 24 Hz represent a combination of filter banks 2 and 5. Then, we create a signal matrix with dimensions of 10 \(\times\) 100 for each imaging period (approximately 1.33% of the value of imaging period characteristics for a channel) that is used as input to CSP to filter and extract features. This is followed by a classification of features. 4. Features reduction for three channels and three bank or band filters (12 Hz interval) has been used to reduce the frequency. From the 22 Hz range (8-30 Hz), the 12 Hz range is selected (45% reduction in the frequency range). Then, a signal matrix of \(18\times 100\) is created for each imaging period (generally generated for all imaging periods), which is 2.4% of one channel. The total features are generally slightly more than two channels. Then, the extracted properties are used as two-dimensional matrices as input to the CSP to filter and extract the properties. After extracting the features, classification is done on them. 5. Feature extraction from 1 to 3 channels is done separately (for each channel, feature extraction and feature selection are made separately). Time domain, frequency domain, wavelet coefficients, and autoregression coefficients were used to extract the features. From each feature extraction method, a certain amount is considered for feature selection. In other words, four parts of features (features related to each proposed method) are used for each channel separately. Feature extraction and feature selection have been implemented for two channels, 8 parts, and for 3 channels, 12 parts. The amount of feature selection is determined separately from each method and channel, and all features from all channels and methods are used for classification. In all the stated cases, 100 features are considered for the channel using algorithms. In other words, the reduction is done from 875 to 100 features. To evaluate the five models mentioned above, we utilized the proposed BGA, PSO, GA, and QGA with ELM classifier for classification. If more than one channel is used, the ability to detect 100 features is the same on all channels.