## 2 Proposed Methods
### Preprocessing and Frequency Band Decomposition  
EEG signals, which reflect the stress caused by audiovisual stimulation, are recorded using noninvasive multichannel electrodes, preprocessed, and analyzed spectrally. An EEG signal is a fine electrical signal tens to hundreds of microvolts (\(\upmu\)V) in magnitude and includes various noise components along with signals generated in the actual brain. As shown in Figure 2, preprocessing consists of four steps and is used to remove noise from EEG signals to facilitate subsequent sentiment analysis. During preprocessing, high- and low-frequency noises are first removed by applying a 4 to 47 Hz bandpass filter to the EEG data. The EEG data is then downsampled to 200 Hz, and ocular artifacts are removed using deep learning-based stacked sparse autoencoder [18]. Subsequently, the spatial difference of the original signal is improved, and temporal information is maintained through the spatial filter [19] to which the eigen decomposition is applied.  
EEG signals have the potential to reveal human neurological change processes because they indirectly measure the electrical activities of nerve cells that make up the brain via electrodes installed in the scalp. These neurological change processes are reflected in different EEG signal frequency bands. For this reason, the preprocessed EEG signals are decomposed into four types, namely \(\theta\) (4 to 7 Hz), \(\alpha\) (8 to 12 Hz), \(\beta\) (13 to 30 Hz), and \(\gamma\) (31 to 50 Hz) [20], depending on the specific frequency and amplitude of the power spectrum acquired by the short-time Fourier transform. The delta (\(\delta\)) band with a frequency range below 4 Hz was excluded in this study because this brainwave is commonly associated with the deep sleep state and is not expected to be highly active under stressful conditions [21]. Additionally, the gamma (\(\gamma\)) band was limited to 50 Hz because the stress state analysis was performed over a frequency domain (0 Hz to 50 Hz) having a relative power of EEG [6]. The power distribution within each frequency band in individual channels is averaged to calculate the dynamic power spectral density (PSD). In a single trial, the EEG signal in each frequency band is divided into a three-second nonoverlapping Hanning window, and the PSD characteristics of the \(k\)-electrode are extracted from each clip to produce a 4 \(k\)-dimensional characteristic vector in all four frequency bands. To effectively extract the stress features corresponding to audio-visual stimuli in each frequency band, the entire multi-channel EEG signal is restructured into a 3D cuboid representing the topographical distribution according to the PSD variation of EEG electrodes, as shown in Figure 3. In this 3D cuboid representation, all PSD relationships between channels, according to the total trials in each decomposed frequency band, are preserved.  
To extract and learn salient features in the stress response from EEG signals, the 3D representation of each acquired frequency band is input into a 3D gated self-attention DNN comprising a 3 DConvB and a gated self-attention block. Figure 4 depicts the detailed architecture of the 3D gated self-attention deep neural network.  
Figure 2: Preprocessing for noise and artifact removal from EEG.  
Figure 4: Architecture of the 3D convolutional gated self-attention deep neural network.  
Figure 3: 3D cuboid representation of the entire multi-channel EEG in each frequency band. PSD distributions of multiple EEG electrodes constitute a topographic map. The PSD variation of EEG electrodes over time forms depth.