# A Two-Step Framework to Recognize Emotion Using the Combinations of Adjacent Frequency Bands of EEG
## 2 Related Work  
On the basis of EEG signals, the emotion recognition technologies mostly concentrate on extracting discriminative features and establishing effective emotion recognition models. The commonly used features have mainly been based on the Fourier transform (FT), wavelet transform (WT), statistics, and entropy [13; 14; 15]. These features have widely been used in current studies, and each has its own set of benefits. Driven by data, [PERSON] et al. [16] proposed a feature extraction method in which the EEG signals were encoded by an echo state network (ESN) and the features were extracted by the recurrent autoencoder, and this method was more effective than the current SOTA method. To save time and increase efficiency, [PERSON] et al. [17] transformed the EEG signals into intrinsic mode functions (IMFs), and the multidimensional information of IMF, which has 8 channels, can be applied for emotion classification. To use the specificity of EEG channels, [PERSON] et al. [18] switched the EEG signals into different sub-bands by the flexible analytic WT so that the information potential could be applied for feature extraction by those sub-bands. To overcome the disadvantages of manual features, [PERSON] et al. [19] proposed a ScalingNet that could dynamically generate many convolution kernels to make a spectral map from the original EEG signal for emotion recognition. The development of a high-performance classifier is another important stage in the EEG-based emotion categorization model. Based on the convolutional neural networks (CNNs), the complex neural network can be designed to produce inspiring emotion recognition results [20; 21; 22]. For example, using an extended CNN model combined with spectrum theory, a graph CNN can learn structural information and different features at the same time [23]. The dynamic graph CNN, which is different from the traditional graph CNN, can take full advantage of the different channel information of EEG data through training the neural network and extracting more discriminative EEG features [24]. The long short-term memory (LSTM), which can avoid gradient disappearance in the algorithm backpropagation process, is applicable for dealing with time-related series problems [25]. The Bi-directional LSTM, which combines the forward and backward data of input on the basis of LSTM, can capture different characteristics through the embedded loop structure and acquire better classification results [26]. Despite the clear benefits of neural-network-based techniques, there are some drawbacks as well. The neural-network-based methods require a lot of training data, and the results largely rely on the adjustment of hyperparameters. However, due to the limitations of equipment, manpower, and other reasons, the EEG dataset with a large sample size is difficult to obtain. To solve this problem, the sparse representation-based classification (SRC) method uses the representation distance of the training set to minimize the regularized residual and determines the classification results by the category that could produce the minimum regularized residual. On the basis of SCR, the collaboration-representation-based classification with regularized least squares (CRC_RLS) method considers the difference in regularized residuals both in the target category and other categories, which has more stable results in pattern classification problems [27]. In decision fusion, the results of each classifier are connected independently to obtain the final result through some rules [28]. To preserve the maximal uniformity of decisions, the Dempster-Shafer (DS) method takes all the pieces of available decisions into account to combine the multimodal results [29]. Through the principle of minimum loss of the training set, adaptive weight learning integrates the decisions through assigning different weights to the results of each classifier [30].