## 1 Introduction
### Preprocessing and Spectral Analysis  
Although there may be differences depending on the recording conditions and devices, in general, EEG signals are recorded at an extremely low-voltage level of approximately 1 to 100 \(\upmu\)V and include various noise signals in addition to signals generated by the brain itself. Specifically, artifacts from eye blinking, which typically have a higher voltage level than normal EEG signals from emotional stimuli, can lead to a loss of emotion-related EEG signals; therefore, they must be removed to improve the accuracy of emotion recognition.  
The preprocessing and spectral analysis are performed on the EEG signals for each brain region. The preprocessing consists of bandpass filtering and downsampling, removal of eye-blink artifacts, and segmentation.  
First, a bandpass filter of 4-47 Hz is applied to the EEG signal, and downsampling was performed to 128 Hz. This removes background noise by maintaining only signals in the 4-47 Hz band, eliminating the rest, and reducing the amount of EEG data through downsampling.  
Second, eye-blink artifacts are removed from the EEG signal using an optimally modified log-spectral amplitude speech estimator and a minima controlled recursive averaging noise estimation (OM-LSA)-based algorithm [34]. This process comprised the following steps--(Step 1) Artifact detection: Using the OM-LSA-based algorithm, the positions of eye-blink artifacts in the EEG signal are detected. In this process, the start and end points of the artifact are found, defining this as the artifact occurrence section (AOS). (Step 2) AOS length calculation: The number of sample data points in the AOS is calculated using information from its start and endpoints. (Step 3) Acquisition of sample data: Previous samples of the AOS length from the AOS start point and subsequent samples of the AOS length from the AOS endpoint are acquired. (Step 4) Overlap-and-add operation [35]: An operation is performed to overlap and add the EEG samples of the two sections acquired in the previous step. Thus, signals similar to EEG signals related to real emotions are generated. (Step 5) Data replacement and loss concealment: The eye-blink  
Figure 2: Example of electrode division by brain region (for 32-channel EEG electrode placement based on the international 10–20 system).  
artifact located in the AOS is replaced with the EEG signal generated in the previous step. Thus, the eye-blink artifact is removed, and the information loss of the EEG signal is concealed.  
Third, the EEG signals for each brain region from which noise and artifacts are removed are divided into 1 s segments, and each segment overlaps by 50%. Each segmented EEG signal is used in the subsequent step of the spectral analysis of each brain region.  
The five main frequency bands of the brain waves are delta (1-4 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (13-30 Hz), and gamma (31-47 Hz), which are associated with different emotional states. By converting these EEG signals into a power spectral density (PSD), different emotional states can be effectively distinguished and recognized through the distribution pattern of the signal power for each band. To this end, each EEG segment is analyzed with a short-time Fourier transform using a 0.25 s sliding window with 80% overlap. Here, the PSD is calculated using the average power in four frequency bands, excluding the delta band related to deep sleep. Thus, the PSD feature expression \(\mathbf{S^{r}}=\left[p_{1}^{r},p_{12}^{r},\dots,\ p_{IN_{r}}^{r}\right]\in \mathbb{R}^{d\times N_{r}\times T}\) for brain region \(R^{r}\) is the output. Here, \(N_{r}\) and \(T\) denote the number of channels and segments in the EEG sequence for the brain region \(R^{r}\), respectively. Furthermore, \(d\) represents the dimensions of the PSD features extracted from a single channel. Feature vector \(\mathbf{S^{r}}\) is used as the input for regional brain-level encoding.