# A Novel Method of Emotion Recognition from Multi-Band EEG Topology Maps Based on ERENet
## 2 Materials and Methods
### An Emotion Recognition Method from Multi-Band EEG Topology Maps Using ERENet
#### 2.3.2 The Model of Proposed ERENet  
In this paper, fully considering the band information of EEG signals and the spatial information between various channels, a lightweight EEG-based emotion recognition network is proposed, inspired by the neuronal circuits in the human brain, as shown in Figure 4.  
Figure 3: The construction of multi-band EEG topology maps. The model mainly includes three modules: a multi-band discrete parallel processing module, a multi-band information exchange and reorganization module, and a weighted classification module. 1. Multi-band Discrete Parallel Processing Module  
The discrete parallel processing circuit architecture is an organizational mode of presenting information in the nervous system. Signals are presented and processed in parallel through discrete information channels [44], as shown in Figure 5. A typical example is the olfactory bulb in the brain: olfactory receptor neurons expressing the same odorant receptors are scattered in the olfactory bulb but send their axons to the same glomerulus to synapse onto the dendrites of their corresponding second-order projection neurons, forming discrete olfactory processing channels [45; 46]. In addition, discrete parallel processing is also characteristic of the visual nervous system. Different bipolar and ganglion cells in the retina form specific connections, and different types of visual signals, such as brightness, color, and motion, are processed in parallel. Compared with serial processing, parallel processing reduces the computational depth, thus reducing the error rate and increasing the processing speed. Figure 4: The structure of ERENet. Figure 5: The discrete parallel processing circuit architecture. Different frequency bands of EEG signals have different degrees of correlation with emotional states. Therefore, EEG signals of different frequency bands can be processed in parallel in discrete information channels based on the principle of discrete parallel processing circuit architecture. Each information channel corresponds to a kind of band signal, and characteristic information specific to the frequency band signal can be extracted in each information channel. The design of the multi-band discrete parallel processing module is shown in the left box in Figure 4. This module is mainly responsible for extracting the feature information specific to each band, and is composed of a group convolution layer, a batch normalization layer (BN) and an activation layer. The BN and the activation layer are not shown in Figure 4.  
In the group convolution layer, the input data \(\left\{f_{\theta},f_{\alpha},f_{\beta},f_{\gamma}\right\}\) are grouped by frequency bands, and each frequency band's data \(f_{i}(i=\theta,\alpha,\beta,\gamma)\) are set with \(\mu\) convolution kernels for convolution, respectively, with stride of 1. The boundaries are filled with 0 to keep the output feature map the same size as the input image. After convolution, each 2D topology map \(f_{i}(i=\theta,\alpha,\beta,\gamma)\) is encoded into a higher-level representation, as shown in Equation (4):  
\[ \begin{split} T^{\theta}&=Conv2D\_\theta(f_{\theta}) =\left\{T_{1}^{\theta},T_{2}^{\theta},\ldots,T_{\mu}^{\theta}\right\},\,T^{ \theta}\in\mathbb{R}^{h\times w\times\mu},\\ T^{\alpha}&=Conv2D\_\alpha(f_{\alpha})=\left\{T_{1 }^{\alpha},T_{2}^{\alpha},\ldots,T_{\mu}^{\alpha}\right\},T^{\alpha}\in \mathbb{R}^{h\times w\times\mu},\\ T^{\beta}&=Conv2D\_\beta(f_{\beta})=\left\{T_{1 }^{\beta},T_{2}^{\beta},\ldots,T_{\mu}^{\beta}\right\},\,T^{\beta}\in \mathbb{R}^{h\times w\times\mu},\\ T^{\gamma}&=Conv2D\_\gamma(f_{\gamma})=\left\{T_{1 }^{\gamma},T_{2}^{\gamma},\ldots,T_{\mu}^{\gamma}\right\},T^{\gamma}\in \mathbb{R}^{h\times w\times\mu},\end{split}  \tag{4}\]  
where \(T_{i}^{j}\in\mathbb{R}^{h\times w}(i=1,2,\ldots,\mu;j=\theta,\alpha,\beta,\gamma)\) represents the \(i\)-th feature map of the band \(j\). In the classical CNN model, a pooling layer is usually added behind the convolution layer to reduce the data dimension, but some information is often lost.