# Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection
## 2 Related Work  
Deep learning is the most popular supervised learning architecture, and some studies are related to the EEG classification by using deep learning. [PERSON] et al. [21] used a BiLSTM to classify the student's brain activity. It gave ten college students courses and classified whether these students feel confused about the contents of courses. They found that the high frequency component of EEG is most important for identifying the emotion. [PERSON] et al. [22] proposed that the existing methods for the emotion recognition based on EEG ignore the spatial information. They constructed a multi-band feature matrix to record the frequency domain and spatial information of EEG. The capsule networks are used to learn the feature from the matrix for classifying emotions. [PERSON] et al. [18] constructed a neural network that can capture the spatial and temporal relationships of EEG electrodes. They considered the neuroscience and mentioned that brain regions play different roles when a human feels a different emotion.  
Feature selection is an important method in EEG signal preprocessing, and existing methods are based on machine learning and statistical-based methods. [PERSON] et al. [23] applied an unsupervised learning method to extract features from EEG. They mentioned that supervised learning methods lead to a decrease in the performance of EEG classification. They utilized an echo state network to construct a recurrent autoencoder for extracting features from EEG. [PERSON] et al. [24] combined principal component analysis (PCA) and t-statistics for feature extraction. It utilized PCA to reduce the dimension of EEG and used t-statistics to select valuable features. According to the experimental results, the neural network performed the best compared to other models. [PERSON] [25] selected features from the frequency domain by using a wavelet transform for the identification system. [PERSON] et al. [26] considered a combination of EEG and functional near-infrared spectroscopy (fNIRS) as features to identify patients with cognitive and motor impairments. The experimental results show that the average of fNIRS and the highest frequency band of EEG have a value for diagnosing.  
Converting the signal from the time domain to the frequency domain for analysis is a common EEG signal preprocessing method. Wavelet transform is a general tool for decomposing EEG to the frequency domain, and many studies applied wavelet transform to analyze the EEG signal. [PERSON] et al. [27] applied wavelet transform to determine amplitudes and frequencies of seizure patients from an adaptive frequency band in the EEG signal. They utilized the wavelet transform to calculate the frequency domain of EEG and used a slice window to extract the patterns from frequency components. These patterns are used to detect the seizure. [PERSON] et al. [28] utilized wavelet transform based on the Fourier-Bessel series expansion to get the frequency domain of EEG. The least-squares support vector machine is used to classify epilepsy. The proposed system can detect epilepsy automatically. [PERSON] et al. [29] applied maximal overlap wavelet transform to decompose EEG signal. The Kruskal-Wallis test was used for the difference in the wavelet variances for detecting seizure and non-seizure. The experimental results show that no pattern can be evidenced for detecting seizures. [PERSON] et al. [30] utilized wavelet transform to extract the frequency domain from EEG and electro-oculogram. The long short-term memory (LSTM) model is utilized to learn these features for detecting diver sleepiness.