## 3 Methods
### Multiband Four-Dimensional Feature Construction
It can be described as:  
\[\text{F}_{\text{PSD}}=\text{PSD}(X_{\text{S}}^{i})=\left\{\left\{\text{f}_{ \text{i}}^{10},\text{f}_{\text{i}}^{1\alpha},\text{f}_{\text{i}}^{1\beta}, \text{f}_{\text{i}}^{17}\right\},\ldots,\left\{\text{f}_{\text{i}}^{0},\text{ f}_{\text{i}}^{1\alpha},\text{f}_{\text{i}}^{1\beta},\text{f}_{\text{i}}^{1 7}\right\}\right\} \tag{8}\]  
\[\text{F}_{\text{DE}}=\text{DE}(X_{\text{S}}^{i})=\left\{\left\{\text{g}_{ \text{i}}^{10},\text{g}_{\text{i}}^{1\alpha},\text{g}_{\text{i}}^{1\beta}, \text{g}_{\text{i}}^{1\gamma}\right\},\ldots,\left\{\text{g}_{\text{i}}^{10}, \text{g}_{\text{i}}^{1\alpha},\text{g}_{\text{i}}^{1\beta},\text{g}_{\text{i} }^{1\gamma}\right\}\right\} \tag{9}\]  
Nevertheless, frequency features can never fully characterize all the feature components of the entire signal. The EEG is obtained from electrodes placed in different regions for acquisition, and the positional relationships between these electrodes contain information about the spatial structure associated with emotions. Therefore, in this paper, a single frequency band frame vector is mapped into a two-dimensional matrix using spatial mapping. References [34; 35; 36] proposed the sparse transform, compact transform, and sensitive transform methods, respectively. The sparse transformation matrix is 19 \(\times\) 19, which undoubtedly requires many computations [34]. The compact matrix of Shen [35] reduces the size to 8 \(\times\) 9, which drastically reduces the computational effort, and the adjacent electrodes in the matrix are more strongly connected, but it is not sensitive to spatial information and does not work well experimentally. Therefore, here, we use the sensitive transformation method of Xu [36] to map it to 9 \(\times\) 9 data. Compared with the first two methods, the connection relationship between electrode points is more in line with the \"10/20\" system while considering the computational effort. These three specific mapping methods are shown in Figure 4. Each frame vector \(\hat{\text{f}}_{\text{i}}^{\text{jY}}\) of a single frequency band is further converted into a two-dimensional matrix. SEED and DEAP each contain 62 and 32 electrode channels, so here, 62 channels are used to map the electrode positions, the corresponding DE and PSD values are filled for the elements with position mapping, and the remaining positionsare replaced by 0. In this way, the window segments transform into a two-dimensional matrix sequence from the perspective of a single frequency band. Taking the \(\theta\)-band as an example, \(\mathrm{S}^{\theta}=\left\{\mathrm{S}^{\theta}_{1},\mathrm{S}^{\theta}_{2},\dots, \mathrm{S}^{\theta}_{\mathrm{j}}\right\}\in\mathrm{R}^{9\times 9\times 2\mathrm{u}}( \mathrm{j}=1,2,\dots,2\mathrm{u})\), it is extended into 3 dimensions in dimensionality. The four-dimensional data of a single feature in the whole window fragment are obtained by fusing the four frequency bands. Taking the DE feature of the window fragment as an example, \(\mathrm{S}=\left\{\mathrm{S}_{(\mathrm{DE})1},\mathrm{S}_{(\mathrm{DE})2}, \dots,\mathrm{S}_{(\mathrm{DE})j}\right\}\in\mathrm{R}^{9\times 9\times 8\times 2 \mathrm{u}}\), considering the problem of information complementarity between different features, we place the same frequency band data of DE and PSD features together to construct the final four-dimensional data \(\mathrm{S}=\left\{\mathrm{S}_{1},\mathrm{S}_{2},\dots,\mathrm{S}_{\mathrm{j}} \right\}\in\mathrm{R}^{9\times 9\times 8\times 2\mathrm{u}}\). After obtaining the mapping matrix of all frame window feature data, Equation (10) is used to normalize the data to a distribution with a mean of 0 and a variance of 1, attempting to avoid poles that may negatively affect the recognition results during gradient descent.