## 3 Results
### Unimodal Data Approaches
#### 3.2.4 Unimodal Physiological Data Approaches  
Based on the analysis of the articles that were classified within the unimodal category with physiological signals, and as shown before in Figure 4 (right portion), the following can be summarized:  
* EEG signals continue to dominate the field of study as they provide direct information about brain activity and fluctuations in brain waves, which can reflect emotional states with high accuracy. EEG is particularly suitable for detecting subtle changes in emotional state and identifying specific patterns in the brain related to emotions. However, it requires the placement of electrodes on the scalp, which can be uncomfortable and limit mobility, and may be more susceptible to artifacts and noise than other physiological signals. During 2022 and 2023, the studies involving emotion recognition with these signals have grown a lot and some of the identified trends are as follows: using EEG signals to enhance human-computer interaction, especially in applications where an intuitive understanding of human emotions is required; some works focus on emotion recognition for patients with disorders of consciousness, during movie viewing, in virtual environments, and in driving scenarios; there is also a trend towards using EEG to aid in the detection and monitoring of mental health issues such as depression. There is a trend of exploring personalization methods in emotion recognition being applied to individual user characteristics, suggesting a direction towards more individualized and user-specific systems. In terms of the analysis models, most works focus on recognizing emotions from EEG signals using a variety of deep learning approaches and signal processing techniques. CNNs, RNNs, and combinations of them are widely used, but there are other trends like the following:
* Attention and self-attention mechanisms: These suggest that researchers are paying attention to the relevance of different parts of EEG signals for emotion recognition.
* Generative adversarial networks (GANs): Used for generating synthetic EEG data in order to improve the robustness and generalization of the models.
* Semi-supervised learning and domain transfer: Allow emotion recognition with limited datasets or datasets that are applicable to different domains, suggesting a concern for scalability and generalization of models.
* Interpretability and explainability: There is a growing interest in models that are interpretable and explainable, suggesting a concern for understanding how models make decisions and facilitating user trust in them.  
Utilization of transformers and capsule networks: Newer neural network architectures such as transformers and capsule networks are being explored for emotion recognition, indicating an interest in enhancing the modeling and representation capabilities of EEG signals.
* Although studies with a unimodal physical approach using signals different from EEG, like ECG, EDA, HR, and PPG, are still scarce, these can provide information about the cardiovascular system and the body's autonomic response to emotions. Their limitations are that they may not be as specific or sensitive in detecting subtle or changing emotions. Noise and artifacts, such as motion, can affect the quality of these signals in practical situations and can be influenced by non-emotional factors, such as physical exercise and fatigue. Various studies explore the utilization of ECG and PPG signals for emotion recognition and stress classification. Techniques such as CNNs, LSTMs, attention mechanisms, self-supervised learning, and data augmentation are employed to analyze these signals and extract meaningful features for emotion recognition tasks. Bayesian deep learning frameworks are utilized for probabilistic modeling and uncertainty estimation in emotion prediction from HB data. These approaches aim to enhance human-computer interaction, improve mental health monitoring, and develop personalized systems for emotion recognition based on individual user characteristics.