## 3 Experiment and Results
### Experimental Methods  
To evaluate the stress recognition performance of the proposed method, other existing techniques were applied for comparison to 3 DCGSA:  
* Support vector machines using entropy features (EF-SVM) [32]: In this method, entropy-based features were extracted from EEG signals decomposed using stationary wavelet transforms to detect mental stress; the obtained signals were then applied to SVMs. * Random forest algorithm with fine-grained EEG spectra (RF-FS) [12]: After decomposing the EEG signals into 99 fine sub-bands rather than 5 EEG sub-bands, the PSD was obtained, and it was then applied to the RF algorithm for stress recognition. * 2D AlexNet-CNN (2D-AlexNet) [33]: Multi-channel EEG signals were converted into 2D spectral images and applied to AlexNet, which comprised five convolutional layers, three max-pooling layers, three fully connected layers, and two dropout layers involved in recognizing the stress state. * Convolutional recurrent neural network (CRNN) [34]: This is a hybrid neural network that combines a CNN and RNN, with the former encoding a high-level representation of an EEG signal and the latter exploring the temporal dynamics of the EEG signal. CRNN was composed of two convolutional layers, one sub-sampling layer, two fully connected recurrent layers, and one output layer. * Pre-layer-norm transformer (PLNTF) [28]: To learn the long-term temporal dependence of multi-channel EEG signals, layer-norm was used before applying the multi-head attention mechanism. Subsequently, the residual connection was applied, and the layer-norm, feedforward, and residual connection were sequentially re-performed. * Gated transformer (GTR) [28]: A gating mechanism was used to stabilize the training process, using GRU instead of the addition operation in the residual connections of the PLNTF structure. * Hierarchical bidirectional gated recurrent unit model with attention (HBGRUA) [14]: HBGRUA comprised two layers, wherein the first layer encoded the local correlation between signal samples of one epoch of the EEG signal and the second layer recognized stress by encoding the temporal correlation between the EEG sequences. The bidirectional GRU and attention mechanism were used at both sample and epoch levels. * Spatial frequency convolutional self-attention network (SFCSAN) [17]: In this method, the EEG signal was decomposed into four frequency bands, and a convolutional self-attention network was applied to the time-frequency entropy values obtained from each frequency band. Furthermore, band mapping was performed between frequencies, and the stress states were recognized using the softmax layer. * 3D residual attention deep neural network (3 DRADNN) [35]: A 3D residual attention neural network was combined with a 3D CNN and a residual attention deep neural network (RADNN). Additionally, RADNN was implemented to improve stress recognition performance and capture local, global, and spatial information. * Spatial frequency 3D convolutional neural network (SF3 DCNN): In this method, the EEG signal was decomposed into four frequency bands and a 3D convolution block was applied to the time-frequency power spectrum value obtained in each frequency band to extract the stress state spatiotemporal features. Thereafter, band mapping between frequencies was performed, and the stress state was recognized using the softmax layer. * Spatial frequency 3D convolutional residual-attention deep neural network (SF3 DCRA): This method uses 3D convolutional block and residual-attention block (using additive operations) in each frequency band to capture the spatiotemporal features corresponding to the stress state, and inputs the final attention characteristic information obtained through inter-frequency band mapping into a linear layer. Then, the stress state is recognized through the softmax function. Except for RF-FS and EF-SVM, all the listed methods involve deep learning; RF-FS and EF-SVM, however, only involve machine learning.