# Multi-Time and Multi-Band CSP Motor Imagery EEG Feature Classification Algorithm
## 4 Discussion  
In order to validate the superiority of the proposed MTF-CSP feature extraction, the baseline methods were compared. Figure 11 compares the proposed MTF-CSP method with the traditional CSP-based models (CSP + SVM, CSP + LDA) in terms of classification accuracy. As shown in Figure 11, the accuracy of the MTF-CSP model based on an AS algorithm and an ED algorithm is both higher than the compared models. Additionally, the proposed MTF-CSP model provides stable results with relatively lower standard deviations.  
Several studies have attempted to create synthetic EEG time-frequency maps to reflect the time-frequency MI-EEG characteristics. As shown in Table 4, typical time-frequency maps and original signals without feature extraction are used as input features to be fed into the typical LDA and SVM models to compare them with our proposed MTF-CSP method. Additionally, the performance of the full-window multi-frequency characteristics was compared to show the effectiveness of the multi-time window strategy. As seen in Table 4, it is obvious that the model using the MTF-CSP features had better performance than the models using the original signal and time-frequency features as inputs.  
In addition to the comparison across models, we compared the average classification accuracy of the AS decision algorithm with the existing Max Voting decision algorithm, as demonstrated in Figures 12 and 13. The Max Voting algorithm works by finding the most frequent values in the sequence [44]. It can be clearly seen that the cross-session  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Features** \\ **Type** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Original Sequence** \\ **Signal** \\ \end{tabular} } & \multicolumn{2}{c}{**STFT**} & \multicolumn{2}{c}{\begin{tabular}{c} **Full-Window** \\ **Multi-Band** \\ **CSP Features** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **Proposed MTF-CSP** \\ **Features Based on 11** \\ **2.5 s-Windows** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Proposed MTF-CSP** \\ **Features Based on 6** \\ **1 s-Windows** \\ \end{tabular} } \\ \hline Classifier & LDA & SVM & LDA & SVM & SVM & ED + SVM & AS + SVM & ED + SVM & AS + SVM \\ \hline sub1 & 0.461 & 0.511 & 0.461 & 0.468 & 0.908 & 0.837 & 0.879 & 0.837 & 0.922 \\ sub2 & 0.542 & 0.521 & 0.493 & 0.542 & 0.549 & 0.563 & 0.577 & 0.549 & 0.599 \\ sub3 & 0.460 & 0.482 & 0.686 & 0.788 & 0.964 & 0.942 & 0.971 & 0.920 & 0.971 \\ sub4 & 0.483 & 0.578 & 0.621 & 0.509 & 0.526 & 0.664 & 0.716 & 0.638 & 0.647 \\ sub5 & 0.563 & 0.674 & 0.504 & 0.533 & 0.696 & 0.659 & 0.696 & 0.719 & 0.748 \\ sub6 & 0.556 & 0.583 & 0.546 & 0.565 & 0.611 & 0.593 & 0.565 & 0.630 & 0.657 \\ sub7 & 0.736 & 0.836 & 0.521 & 0.514 & 0.779 & 0.821 & 0.814 & 0.650 & 0.814 \\ sub8 & 0.500 & 0.567 & 0.545 & 0.537 & 0.925 & 0.940 & 0.925 & 0.925 & 0.925 \\ sub9 & 0.523 & 0.562 & 0.731 & 0.831 & 0.800 & 0.785 & 0.815 & 0.808 & 0.800 \\ \hline Avg & 0.536 & 0.590 & 0.568 & 0.587 & 0.751 & 0.756 & 0.773 & 0.742 & 0.787 \\ Std & 0.079 & 0.101 & 0.087 & 0.122 & 0.155 & 0.134 & 0.137 & 0.128 & 0.127 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of classification accuracy of original timing signals, time–frequency features, and MTF-CSP features.  
Figure 11: Comparison of classification accuracies of the proposed MTF-CSP based models with the traditional CSP-based models.  
classification accuracy obtained by the AS decision algorithm is higher than that obtained by the Max Voting algorithm in all cases.  
Although this study extracted EEG motor imagery features from different angles by proposing the MTF-CSP and obtained a better recognition cross-session motor imagery EEG ability than the existing methods. It has not been integrated an end-to-end system and is inconvenient for practical applications. We will try to study the current model in some end-to-end models and will apply these data to our MI-EEG decoding work.