# Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection
## 5 Experimental Results
### Results and Analysis  
The maximum marginal approach is utilized to select the features, we apply the selected features to the BiLSTM model for detecting emotions. K nearest neighbor (KNN), convolutional neural network (CNN), LSTM, and neural network (NN) are selected as baselines to validate the proposed approach. KNN is a machine learning model that detects emotions based on the class of the nearest neighbor. NN is utilizing non-linear functions to extract the features from the EEG signal for detecting emotions. It does not consider the time of EEG signals. CNN utilizes convolutional kernels for the local sampling of input. LSTM is based on the recurrent neural network that utilizes forget gates to drop some information that is not useful in detecting emotions.  
Table 1 shows the comparison results under the discrete wavelet transform on level 1. According to the results, MM-based BiLSTM achieves the highest performance on the accuracy, precision, and F1-score. The reason is that BiLSTM model considers the bi-directional temporal information of the EEG signals. Since the precision and the recall affect each other, the recall of MM-based BiLSTM is lower than LSTM. Overall, in the results, MM-based classifications perform better than the classifications without signal preprocessing. This indicates that the proposed maximum marginal approach can improve the performance of emotion detection.  
Figure 7 shows the performance of the discrete wavelet transform by using MM-based BiLSTM, where the x-axis represents the level, and the y-axis represents the value of each evaluation metrics. The proposed approach achieves the best performance at level 1 with an accuracy of 0.86. With the improvement of the level, accuracy, precision, and F1-score show a downward trend. When the level is  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1-score**} \\ \cline{2-7}  & **Original** & **MM** & **Original** & **MM** & **Original** & **MM** & **Original** & **MM** \\ \hline KNN & 0.501 & 0.540 & 0.486 & 0.537 & 0.256 & 0.453 & 0.30  
greater than 6, these three evaluation metrics of the method tends to be stable. With the increase in the level, the small features are difficult to be selected, so the metrics of emotion detection has decreased. When the level is greater than 6, the recall of the method has decreased.  
Table 2 shows the comparison results between different feature selection methods. We utilized the wrapper, built-in, filter, and fisher score to conduct a comparison experiment. For the wrapper method, we input the EEG signal at each time point to obtain the performance. The features of the wrapper method are selected with the best performance. The built-in method is to select the features by obtaining the weights of the BiLSTM model's weights. The Fisher score is selecting the features by calculating the variances of two classes of EEG signals. The proposed maximum marginal approach achieves the best performance on the accuracy, precision, and F1-score. In addition, it achieves a lower result than wrapper and fisher score on the recall.