## 2 Case Study: Digitizing Human Emotions
### Progress in EEG-Based Emotion Recognition
Although the brain map activity, a virtual representation of the EEG signal, is not currently used for emotion detection, it presents potential for future applications involving convolutional neural networks. Researchers are employing a variety of features extracted from EEG signals, including those from the time domain [63], frequency domain [53], and mixed domain features [64]. Multimodal systems may integrate EEG signals with one or more other types of input signals. These additional inputs can include skin conductance, facial expressions, eye movement, muscle activity, or other vital signs, used in conjunction with EEG to create a comprehensive multimodal system. Among these inputs, facial expressions are frequently used in conjunction with EEG in emotion recognition systems due to their high accuracy relative to other signals [42; 45; 52; 56]. The effectiveness of these systems in detecting different emotions varies, with accuracies ranging from 83% to 89.6%. The highest accuracy, 89.6%, was achieved by [PERSON], [PERSON] in 2021 [45] using 15 EEG channels. This system utilized signal processing to extract wavelet features from the EEG signals, recognizing a four-class emotions model: happiness (high arousal-high valence (HA-HV)), anger (high arousal-low valence (HA-LV)), and sadness (low arousal-low valence (LA-LV)). EEG signals have also been incorporated into a multimodal system along with galvanic skin conductance and blood volume pressure, using 15 EEG channels [65]. The accuracy of this system was approximately 75% for three different emotional states. Table 1 presents a comparison of various EEG-based emotion recognition techniques. Figure 4: The optimal locations for all 62 EEG electrodes on the human scalp [41]. Moreover, the integration of EEG with the IoT has the potential to develop applications designed to enhance cognitive performance, improve physical and mental well-being, and aid in comprehending the human brain and its functions. For example, technology that utilizes EEG sensors and IoT-connected devices could be used to detect and interpret signals associated with emotions. This would enable users to gain a deeper understanding of, and potentially exert greater control over, their conscious and subconscious behaviors. Additionally, this technology could be applied to create applications aimed at boosting memory, concentration, and creativity by analyzing and interpreting cognitive processes [1; 24; 67]. Numerous studies and surveys have concentrated on classifying emotions and developing physical systems to achieve the highest possible accuracy [68]. These activities comprehend the application of various machine learning techniques and the investigation of different methods for feature extraction and frequency range analysis [69]. Additionally, some research has been directed toward optimizing the selection of channels for emotion recognition processes [70] and monitoring other health conditions [71]. Furthermore, general daily movements and actions were analyzed and mapped using EEG signals in [72]. However, relatively few studies have focused on the implementation of secure EEG biometric data transfer between servers and clients within the framework of IoT solutions over 5G wireless networks [67; 73; 74; 75].