## 4 Discussion of Results
### General Discussion about Combinations of Filter Banks  
The best results for the new signals with CSP and three classifiers are compared with different previous CSP and FBCSP methods. The advantages of these new contributions are presented for comparison. Emphasis is placed on solving the problems mentioned in the introduction by identifying the highly distinctive features of brain activities for improved classification. It is clear from the results that the proposed methods obtain better values than previous methods. Therefore, this subsection compares the effectiveness of our proposed method with previous methods. Finally, in the next subsection, we discuss our contributions and results. This explains the problems and provides the main solution for solving these problems. Based on [22; 23], important distinctive patterns of brain activation, such as the left hand and right hand, are in the 4 Hz to 40 Hz frequency domain. Extracting features from the large-scale frequency domain produces the most similar and distinctive patterns, which leads to the extraction of ineffective features. Considering suitable frequency domains helps researchers extract effective features by separating similar patterns from most of the distinctive patterns. Based on [20; 23], it is necessary to find small and suitable frequency bands with important distinctive patterns for high-accuracy classification. Based on most research [15; 16; 17; 18; 19; 20; 21; 22; 23], the most distinctive patterns are distributed in different frequency domains as small, fixed filter banks. The distribution of distinctive patterns in the 4-40 Hz frequency domain led us to consider the combination of two limited filter banks (frequency domains) to discover the most important distinctive patterns of brain activity signals. New combinations of signals can express low- and high-accuracy classifications by measuring distinctive patterns with a combination of different frequency bands, which express the support rate of the most distinctive patterns as a distinctive formula. Unlike most previous papers [15; 16; 17; 18; 19; 20; 21; 22; 23], this study focuses on the combination of filter banks (limited frequency bands) using general CSP methods and a Lagrangian polynomial equation for extracting coefficients (features). The purpose of combining the two filter banks is to solve the previous problems. These problems are as follows. (1) In large frequency ranges, it is not possible to distinguish the most accurate location of the most distinctive patterns. In other words, the frequencies cover distinctive patterns of brain activity (left and right). The most distinctive patterns can be lost when creating smaller frequency bands. (2) The amount of noise is very high in high-frequency bands. (3) Reducing the quality of narrow filter banks leads to more degradation of raw EEG signals. This is the general composition model (various models) that has been described. In this study, as a small part of our ordinary model, two filter banks were implemented as new combined signals to eliminate these problems to some extent. In our study, the kappa of FB 5 and 6 with CSP for the left and right hands was improved by 5%, the kappa of FB 3 and 9 with CSP for the hand and foot was improved by 10%, and the accuracy of FB 5 and 6 with PCA was improved by 18% for a single channel. Therefore, for the two brain activities, the hand and foot achieved high accuracy and kappa, which include 4 Hz to 8 Hz (theta) with 8 Hz to 12 Hz (alpha). And 16-36 Hz (beta) had more noise, and the distinctive patterns were not high in other combinations similar to these combinations. In addition, statistical significance was calculated and was a suitable value. The purpose of the new combinations of signals is to increase the classification accuracy and discover the effectiveness of the two combination sub-bands, which leads to a new signal reaching the near-optimal signal. However, it does not reach the global optimal signal.