# Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection
###### Abstract  
Emotion detection is an important research issue in electroencephalogram (EEG). Signal preprocessing and feature selection are parts of feature engineering, which determines the performance of emotion detection and reduces the training time of the deep learning models. To select the efficient features for emotion detection, we propose a maximum marginal approach on EEG signal preprocessing. The approach selects the least similar segments between two EEG signals as features that can represent the difference between EEG signals caused by emotions. The method defines a signal similarity described as the distance between two EEG signals to find the features. The frequency domain of EEG is calculated by using a wavelet transform that exploits a wavelet to calculate EEG components in a different frequency. We have conducted experiments by using the selected feature from real EEG data recorded from 10 college students. The experimental results show that the proposed approach performs better than other feature selection methods by 17.9% on average in terms of accuracy. The maximum marginal approach-based models achieve better performance than the models without feature selection by 21% on average in terms of accuracy. Keywords:signal preprocessing; signal similarity; emotion detection +
Footnote †: journal: Medical  
weights are dropped since they are not useful for emotion detection. These approaches focus on the difficulty of EEG-based emotion detection, which discovers the correlation between features and emotions from EEG signals. To select the valuable features, we propose an approach on EEG signal preprocessing to select features. The selected features indicate the least similar segments between two classes of EEG signals. The signal similarity is defined as follows. **Definition 1** (Signal similarity). : _The signal similarity is described as the distance between two signals \(d_{i}\) and \(d_{j}\), which is formulated as \(D(d_{i},d_{j})=\sqrt{||d_{i}-d_{j}||^{2}}\). The signal similarity is negatively related with the distance._  
Figure 1 gives the segments of two EEG signals with different emotions on two time intervals. The similarity between the two signals at the first time interval is smaller than the second time interval. The segments at the first time interval are selected as the features for detecting emotions. The proposed method selects the features from the frequency domain of EEG. The frequency domain is helpful to preprocess and analyze EEG. The EEG signal on time domain is hard to analyze since it usually combines with noise caused by different behaviors. To research the EEG produced by specific behavior, we have to remove the noise. The frequency of noise is different from regular brain waves so that the noise can be detected on the frequency domain. Wavelet transform is a popular tool for time-frequency analysis [19; 20]. It overcomes the shortcoming of short-time Fourier transformation that the filter window size does not change with frequency. It utilizes a wavelet to decompose EEG into an approximate component and a detail component. The approximate component consists of the low frequency band of EEG, and the detail component consists of a high frequency band. The difference of EEG signals is reflected in the high frequency band so that the features are selected on the detail component. The main contributions of this study are as follows. * We propose a maximum marginal approach (MM) on the EEG signal preprocessing for emotion detection. It defines the similarity of two class signals and selects the feature on the frequency  
Figure 1: Example of the maximum marginal feature. domain. The results show that MM performs better than other feature selection methods on emotion detection. * We conducted experiments on real EEG data by applying the selected features to bi-direction long short-term memory model (BiLSTM). The results show that the MM-based models performed better than the models without signal processing. The MM-based BiLSTM achieved better performance than others.