## 3 Methods
### Multiband Four-Dimensional Feature Construction  
In this section, the process of forming a 4D matrix based on the temporal-frequency-spatial is explained in detail, as shown in Figure 2.  
Since the integration of temporal and frequency band information of all available channels requires a three-dimensional spatial representation, this requires the consideration of a matrix of filtered signals obtained from all channels. Take the DEAP dataset as an example, which includes a total of 1280 sample data points (32 subjects \(\times\) 40 experiments). For the problem that a small quantity of data can cause overfitting when the network layers are deep, we reduce this effect by increasing the number of samples. Specifically, for the signal \(X_{\text{trial.mov}}\in\text{R}^{\text{M}\times\text{N}_{2}}\) after baseline removal, a nonoverlapping windowing process with a time duration of u seconds (design in this paper with u = 5 s) is performed to obtain the divided data \(\left\{X_{\text{S}}^{1},X_{\text{S}}^{2},\ldots,X_{\text{S}}^{n}\right\}\), where the ith window segment \(X_{\text{S}}^{i}=\left\{x_{1},x_{2},\ldots,x_{\text{M}}\right\}\in\text{R}^{ \text{M}\times\text{R}}(i=1,2,\ldots,n)\), \(x_{\text{c}}\in\text{R}^{\text{R}}(c=1,2,\ldots,M)\) denotes data from the c-th electrode channel in \(X_{\text{S}}^{i}\), and R is the sampling frequency of the whole window segment. Different frequency band data in EEG reflect different emotional information, as detailed in Table 1[32]. While conducting directional research, the linear data phase is commonly required to be high. A finite impulse response filter (FIR) can obtain a rigorous linear phase with high stationarity and low interference power caused by operational errors. Therefore, we use FIR to have a sub-band division for each window segment  
Figure 2: 4D feature extraction process. [PERSON] [25] experimentally compared the EEG emotion recognition results under different band combinations and found that band combinations of \(\alpha\), \(\beta\), and \(\gamma\) had the highest accuracy in task recognition. In addition, [PERSON] [33] remarked that the \(\theta\)-band features are closely correlated with arousal. Therefore, in this paper, the four bands \(\theta,\alpha,\beta,\gamma\) were chosen to study the emotional state features of EEG signals. Through the FIR filter, each window segment was decomposed into \(\chi^{i}_{S}=\left\{\left\{\mathrm{f}_{1}^{\theta},\mathrm{f}_{1}^{\alpha}, \mathrm{f}_{1}^{\beta},\mathrm{f}_{1}^{\gamma}\right\},\ldots,\left\{\mathrm{f }_{M}^{\theta},\mathrm{f}_{M}^{\alpha},\mathrm{f}_{M}^{\beta},\mathrm{f}_{M}^{ \gamma}\right\}\right\}\right\}\)\(\mathrm{(i=1,2,\ldots,n)\). The specific realization formula is as follows:  
\[\mathrm{h}(n)=\mathrm{h}_{d}(n)\cdot\mathrm{w}(n)=\frac{\mathrm{sin}[(\mathrm{ M}-\tau)\cdot\mathrm{W}_{\mathrm{h}}]-\mathrm{sin}[(\mathrm{M}-\tau)\cdot \mathrm{W}_{\mathrm{l}}]}{\pi\cdot(\mathrm{m}-\tau)}\cdot\mathrm{w}(n) \tag{3}\]  
\[\mathrm{H}_{d}(\mathrm{e}^{\mathrm{jw}})=\sum\limits_{n=-\infty}^{n=+\infty} \mathrm{h}_{d}(n)\mathrm{e}^{-\mathrm{jwn}} \tag{4}\]  
\[\mathrm{H}(\mathrm{w})=|\mathrm{H}(\mathrm{e}^{\mathrm{jw}})|=|\frac{1}{2\pi} \int\limits_{-\pi}^{\pi}\left(\mathrm{H}_{d}(\mathrm{e}^{\mathrm{j\theta}}) \mathrm{e}^{-\mathrm{j\theta M}/2}\right)\mathrm{W}(\mathrm{e}^{\mathrm{jw}}) \mathrm{d}\theta| \tag{5}\]  
where \(\mathrm{h}(n)\) is the filter coefficient, \(\mathrm{H}_{d}(\mathrm{e}^{\mathrm{jw}})\) is the corresponding frequency response, \(\mathrm{H}(\mathrm{w})\) is the amplitude-frequency response function, \(\mathrm{h}_{d}(n)\) is the unit impulse response, \(\mathrm{w}(n)\) is the window function, \(\mathrm{W}_{\mathrm{h}}\) and \(\mathrm{W}_{\mathrm{l}}\) are the cut-off frequencies of the bandpass filter, \(\tau=\frac{\mathrm{M}-1}{2}\), and \(\mathrm{M}\) is the number of filter steps.