## 2 Background  
Virtual reality (VR), as a transformative technology, has redefined human-computer interactions by creating immersive three-dimensional experiences that transcend traditional interfaces [9]. Despite its advancements, the exploration of VR's potential for customization and adaptability, especially in the realm of emotion recognition and response, remains relatively nascent. The ability to accurately classify and respond to user emotions could initiate a new era of VR applications, one that is more intuitive, interactive, and tailored to individual experiences [10]. However, the complex nature of human emotions poses significant challenges in achieving precise emotion recognition within VR environments. The integration of biosignal processing techniques with advanced machine learning algorithms offers a promising approach to navigate the complex landscape of human emotions in VR [11]. Furthermore, the development of comprehensive datasets, such as DER-VREEG, which take advantage of low-cost wearable EEG headsets in VR settings, has facilitated the advancement of emotion recognition algorithms [12]. The exploration of embodied interactions within VR systems also provides valuable insights into user experiences and emotional responses, underscoring the importance of a nuanced understanding of human-VR interactions [13]. Moreover, the analysis of VR as a communication process highlights the potential of VR to mediate complex emotional and cognitive experiences [14]. Finally, the examination of variation in VR gameplay and its impact on affective responses illuminates the complex relationship between VR experiences and emotional states [15]. Traditionally, emotion recognition methods have predominantly utilized facial expression analysis and speech patterns to discern emotional states [16; 17; 18]. Although effective to a certain extent, these techniques may not fully translate to the virtual reality (VR) environment due to user comfort considerations and the inherent limitations of VR headsets. This limitation calls for the exploration of more subtle and less invasive techniques for emotion recognition within VR settings. Biosignal processing, which involves the interpretation of physiological signals such as electrocardiography (ECG) and galvanic skin response (GSR), presents a viable alternative [19]. These biosignals are closely related to human emotional responses and, as such, offer a promising avenue for emotion classification that bypasses the constraints of traditional methods. In the domain of emotion recognition through physiological signals and machine learning, seminal works have established a solid foundation for our investigation. [PERSON] et al. [20] offered an comprehensive review, defining current challenges and prospective avenues in the field, particularly highlighting the significant role of machine learning techniques and physiological indicators in emotion recognition. [PERSON] et al. [7] developed an algorithm based on electrocardiography (ECG) for the detection of emotions, employing a series of experiments involving varying durations of listening to music to evoke different emotional responses. The ECG signals recorded during these sessions were subjected to feature selection and reduction using machine learning approaches, resulting in classification accuracies of 82.78% for valence, 72.91% for arousal, and 61.52% for a four-class emotion model. In addition, [PERSON] et al. [21] explored the classification of emotional valence using electroencephalogram (EEG) signals within a virtual reality (VR) setting. This study extracted differential entropy features from various EEG bands and evaluated the accuracy of several classifiers, including support vector machines (SVM), k-nearest neighbor (kNN), naive Bayesian, decision trees, and logistic regression, with the SVM classifier achieving an accuracy of 76 22%. [PERSON] et al. [17] focused on emotion recognition using galvanic skin response (GSR) signals, proposing a novel approach utilizing quantum neural networks.