## 5 Discussion
#### Potential Implications
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Description** & **Accuracy** & **Reference** \\ \hline SVM (class weight kernel) & Used for emotion classification with EEG signals in VR & 85.01\% & [PERSON] et al. (2012) \\ \hline EEG signals with XGBoost classifiers & Best performance among tested classifiers & - & [PERSON] et al. (2019) \\ \hline Eye fixation vs. pupil diameter & Eye-tracking features for emotion classification & 75\% (eye fixation) & [PERSON] et al. (2019) \\ \hline KNN and SVM with heart rate & Emotion prediction in VR & 82\% & [PERSON] et al. (2019) \\ \hline Pupil position with SVM & Emotion classification using pupil position & 59.19\% & [PERSON] et al. (2019) \\ \hline Heart rate and electrodermography with SVM & High accuracy for multi-class emotion classification & - & [PERSON] et al. (2019) \\ \hline SVM with heart rate and IBI & Classify emotions in four quadrants & - & [PERSON] et al. (2019) \\ \hline SVM with eye-tracking data & Emotion classification using eye-tracking & 57.65\% & [PERSON] et al. (2019) \\ \hline Wearable biosensors & Detect emotions using heart rate, EDA, and EEG & - & [PERSON] et al. (2019) \\ \hline AffectivelyVR with KNN and GSR sensors & Personalized emotion recognition & 96.5\% & [PERSON] et al. (2019) \\ \hline Semi-supervised GANs & Improved emotion classification method & - & [PERSON] et al. (2019) \\ \hline Brainwave sensors and eye-tracking & Predict user’s attraction on visual stimuli & - & [PERSON] et al. (2019) \\ \hline Biosignals for affective feedback & Indicators of emotional state in VR & - & [PERSON] \& [PERSON] (2019) \\ \hline General purpose features from biosignals & Compared to traditional domain-specific features & - & [PERSON] et al. (2019) \\ \hline Biosignals and game parameters for cybersickness & Determine occurrence of cybersickness & 82\% (binary), 56\% (ternary) & [PERSON] et al. (2019) \\ \hline Database for emotional analysis with EEG & Using YouTube 360 videos for emotion classification & - & [PERSON] et al. (2019) \\ \hline K-ELM for stress levels & Classification of stress levels in VR & \textgreater{}95\% & [PERSON] et al. (2019) \\ \hline Interoceptive attribution model & Framework for emotion research in VR & - & [PERSON] et al. (2019) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison with related work. triggering scenarios (e.g., exposure therapy for anxiety disorders) while continuously monitoring the patient's emotional responses. Therapists could utilize real-time data, such as heart rate variability (HRV) and galvanic skin response (GSR), to assess the patient's progress and adjust therapeutic interventions accordingly. For example, when heightened anxiety is detected, the virtual environment could automatically shift to a calming scene, or the therapist could intervene to help the patient manage their emotional state. This real-time monitoring allows for dynamic adjustments, enhancing the effectiveness of therapeutic interventions by tailoring the treatment to the patient's emotional condition. Entertainment and Gaming: The gaming industry could leverage emotion recognition to create more immersive and personalized gaming experiences. By detecting players' emotions in real time, developers could dynamically adjust in-game events, challenges, and storylines based on the player's level of engagement. For instance, if a player exhibits signs of boredom, the system could increase the game's difficulty or introduce new, stimulating elements to re-engage the player. This real-time adaptation would enhance immersion, personalize gameplay, improve user satisfaction, and potentially extend playtime. Virtual Training and Simulation: Emotion recognition could play a pivotal role in virtual training and simulation environments, such as those used in military, medical, or emergency response training.