## 2 Case Study: Digitizing Human Emotions
### Progress in EEG-Based Emotion Recognition  
Among various artificial intelligence approaches, deep learning has shown the most potential and has been extensively utilized in the study of human emotions [41; 42; 43]. Most deep learning models are trained on extensive datasets, containing labeled facial images [42; 44; 45], audio waves [46], and textual data [47; 48]. These models utilize extracted features to classify emotions with a higher degree of accuracy than traditional methods. Furthermore, researchers are currently investigating multimodal approaches that integrate two or more types of inputs, such as visual and textual data, to achieve more precise representations of emotions [48; 49]. Computer vision methods, such as gaze and body gesture tracking, have been proposed [49] to capture subconscious emotional indicators. Emotion recognition employs a diverse array of methodologies to accomplish its goals. Initially, the focus was on analyzing facial expressions, which resulted in the creation of a highly efficient and subject-independent system for emotion detection. This process involves identifying specific facial features (like the eyes, eyebrows, and mouth) and comparing these features against a predefined set of emotions [44; 45; 50]. For example, aspects such as the shape of the eyes, the movement of the eyebrows, and the curvature of the mouth can be employed to identify emotions like happiness, sadness, anger, or surprise [51]. After identifying these facial features, a computer vision algorithm can be applied to recognize the facial expression and categorize it into one of the predetermined emotions. Initially, facial expression recognition exhibited low efficiency, but with technological advancements, including high-definition (HD) cameras, and the implementation of deep learning and machine learning algorithms, there has been significant improvement in efficiency. In certain instances, the accuracy of recognition has surpassed 95% [42; 44; 50; 52; 53]. A primary advantage of using facial expressions for emotion recognition is their subject-independent nature, as they tend to be relatively consistent across different individuals. However, relying solely on facial expressions for emotion detection presents a significant challenge due to their potential to be feigned. For example, an individual might force a smile despite feeling sad or depressed, or display tears of joy, which could be challenging for systems based solely on facial expressions to accurately discern. Moreover, these systems necessitate a continuous HD camera focused on the subject's face to monitor facial expressions consistently, enabling the analysis and extraction of human emotions. In addition to facial expressions, other vital signs have been employed for emotion recognition, including speech [54], human posture, and functional magnetic resonance imaging (fMRI) [55]. Conversely, with notable advancements in portable, user-friendly EEG headsets, brain signals have become increasingly common for emotion recognition in both single and multimodal systems [45; 56; 57]. While EEG signals are utilized in this domain, there is a notable variation in the features extracted, as evidenced in prior studies [58]. Techniques for emotion recognition often employ both time-based and frequency-based features. Regarding the number of channels and their optimization for use in neural networks for recognition, researchers have investigated 62 different channels on the human scalp, as delineated in the 10-20 system illustrated in Figure 4. These 62 channels, cataloged in an online database, have been employed in numerous studies using various training techniques, predominantly the support vector machine (SVM) method [13; 59; 60; 61]. Thechannels are distributed with 27 on the left side of the scalp, 27 on the right side, and the remaining 8 along the midline of the scalp. Most research in this field concentrates on the EEG signal itself rather than on brain mapping activities [60; 62].