## 3 Experiment and Results
### Experimental Methods  
To evaluate the emotion classification performance of the proposed method and compare it with other neural network models, the various methods listed below were applied in the experiment.  
* CNN: This method constituted two convolution layers with four 3 \(\times\) 3 convolutional filters, two max-pooling layers with a 2 \(\times\) 2 filter size, a dropout layer, two fully connected layers, and a softmax layer;
* LSTM: LSTM was applied rather than a CNN. It comprised two LSTM layers: a dropout, fully connected and a softmax layers. The number of hidden units of the lower- and upper-layer LSTMs were 128 and 64, respectively. The fully connected layer contained 128 hidden units;
* BGRU: BGRU was used rather than LSTM. The number of hidden units in the forward and backward GRU layers was 64;
* Convolutional recurrent neural network (CRNN) [24]: A hybrid neural network consisting of a CNN and an RNN for extracting spatiotemporal features was applied to multichannel EEG sequences. This network consisted of two convolution layers, a subsampling layer, two fully connected recurrent layers, and an output layer;
* Hierarchical-attention-based BGRU (HA-BGRU) [25]: The HA-BGRU consisted of two layers. The first layer encoded the local correlation between samples in each epoch of the EEG signal, and the second layer encoded the temporal correlation between epochs in an EEG sequence. The BGRU network and attention mechanism were applied at both sample and epoch levels;
* Region-to-global HA-BGRU (R2G HA-BGRU): the HA-BGRU network was applied to extract regional features within each brain region and global features between regions;
* R2G transformer encoder (TF-Encoder): in the R2G HA-BGRU method, transformer encoders were applied instead of BGRU with attention mechanism;
* R2G hierarchical spatial attention-based BGRU (HSA-BGRU): only temporal encoding in a regional brain-level encoding module was used, and the same method as the HSCFLM was applied for the rest;* HSCFLM: This method mainly consisted of a regional brain-level encoding module, a global brain-level encoding, and classification module. This is the proposed method.  
The CNN, LSTM, BGRU, CRNN, and HA-BGRU methods apply multichannel EEG signals to each neural network without dividing the brain into regions. However, the R2G HA-BGRU, R2 GTF, R2G HAS-BGRU, and proposed HSCFLM methods divided the brain into nine defined regions, grouping the corresponding electrodes by region. Among them, a soft attention mechanism was applied to the R2G HA-BGRU method, while a self-attention mechanism was applied to the other methods.  
Accuracy was used to evaluate the performance of each method and is defined by Equation (22):  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{22}\]  
where true positive (TP) refers to the number of positive data points correctly classified as positive. True negative (TN) refers to the number of negative data points correctly classified as negative. False positive (FP) refers to the number of negative data points incorrectly classified as positive. False negative (FN) refers to the number of positive data points incorrectly classified as negative. The accuracy of the model is defined as the ratio of correctly classified data points to the total number of data points.