## 2 Case Study: Digitizing Human Emotions
### EEG Databases for Emotion Recognition  
In the classification of human emotions through EEG signal recordings, it is a standard practice to use musical videos or short clips to evoke a range of emotional responses from participants. The duration of these clips can range from a few seconds to six minutes, where participants may display their emotions in diverse ways. Despite the growing interest in exploring the influence of emotions on brain signals, there remains a notable lack of \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Ref.** & **Channels** & **Accuracy** & **Subject** & **Database** & **Detection** \\ \hline
[41] & 62 & N/A & Dependent & Seed & Positive \\  & & & & & Neutral \\  & & & & & Negative \\ \hline
[42] & 14 & 77.6–78.96\% & Dependent & DEAP & Valence \\  & & & & & Arousal \\  & & & & & Dominance \\ \hline
[43] & 12 & 81.5–86.87\% & Independent & 12 subjects & High \\  & & & & & Low Valence \\  & & & & & Arousal \\ \hline
[66] & 14 & 87.25\% & Dependent & 19 subjects & Happiness and \\  & & & & & Sadness \\  & & & & & Fear and Anger \\  & & & & & Surprise and Disgust \\ \hline
[44] & 32 & 96.28–96.62\% & Dependent & DEAP & High \\  & & & & & Low Valence \\  & & & & & Arousal \\ \hline
[45] & 62 & 83.33\% & Independent & Seed & Neutral \\  & & & & & sadness and fear \\  & & & & & happiness \\ \hline
[46] & 32 & N/A & Dependent & MAHNOB & High \\  & & & & & Low Valence \\  & & & & & Arousal \\ \hline
[47] & 10 & 58.47–60.90\% & Independent & N/A & High \\  & & & & & Arousal \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between various EEG-based emotion recognition methods. readily available databases for such studies. Table 2 presents a summary of five publicly available datasets. Most of these databases are aimed at identifying emotions across three primary dimensions: valence, arousal, and dominance. Arousal reflects the spectrum from passive disinterest to stimulated enthusiasm, whereas valence covers the spectrum from tense unhappiness to joyfulness. The dominance dimension comprehends feelings, ranging from vulnerability to a sense of empowerment, as represented in the illustration provided in Figure 5.  
The self-assessment manikin (SAM) is the most widely used standard for reporting emotions in studies [76; 77; 78]. This method involves presenting manikins to users, who then describe their emotional state according to a linear scale on each emotional axis, as illustrated in Figure 5. A significant limitation of the databases currently using SAM is that participants are required to represent their feelings throughout an entire video or experimental trial with a single value. However, it is more realistic to consider that a participant's emotions may vary during the course of the experiment. The DEAP database [79] includes 32 recordings from 32 subjects engaging in various emotional processing tasks, making it a popular resource in research. Each participant viewed 40 one-minute-long videos that acted as emotional stimuli. During each of the two experimental sessions, a two-minute baseline recording was conducted. A total of 32 electrodes, positioned according to the international 10-20 system, captured physiological data, including EEG and GSR, at a sampling rate of 512 Hz, which was then downsampled to 128 Hz for analysis. The data were divided into training and testing sets to facilitate research. Additionally, the database includes extra information, such as measurements