## 2 Background
Given the diverse levels of information encapsulated in the features of the GSR signal corresponding to different emotions, the quantum neural network, optimized with particle swarm optimization, facilitated a more adaptable classification scheme, achieving an average accuracy of 84. 9% in five emotion categories with data from 35 participants. In the evolving field of emotion recognition through physiological signals, several studies have pioneered the use of singular biosignals for sophisticated classification tasks. [PERSON] et al. [22] investigated the efficacy of artificial neural networks by harnessing heart rate variability (HRV) data to classify emotions into five distinct categories: pleasure, happiness, fear, anger, and neutral. HRV data, obtained from wristband monitors during gameplay, facilitated the training of neural networks, with a notable configuration that achieved an average accuracy of 82.84%. In a quest to discern three emotional states, [PERSON] et al. [23] used photoplethysmography (PPG) and galvanic skin response (GSR) signals collected via an instrumented glove, employing classifiers such as SVM and LDA. Interestingly, the combination of GSR signals and the SVM classifier achieved a perfect accuracy rate in identifying amusement, sadness, and neutral states. Further exploring the domain of wearable technologies in healthcare, [PERSON] et al. [24] introduced an algorithm that assesses emotional states through the dimensions of arousal and valence using various machine learning techniques. Their research demonstrated the superior accuracy of a fused model incorporating a respiratory belt, PPG, and fingertip temperature signals, underscoring the potential of multimodal physiological data to enhance emotion recognition accuracy. The highest accuracies recorded were 73.08% for arousal and 72.18% for valence. [PERSON] et al. [25] investigated the utility of machine learning classifiers in a VR setting aimed at acrophobia treatment, utilizing EEG, GSR, and PPG signals to determine fear levels to tailor VR therapies. Although a 2-choice fear scale yielded an accuracy rate of 89 5%, the performance was markedly reduced with a more granular 4-choice scale. Complementing these empirical studies, [PERSON] et al. [26] provided a comprehensive review on emotion recognition within immersive VR, charting the field's progression and setting the stage for future inquiries. These seminal contributions illustrate the importance of emotion recognition techniques and provide invaluable insights, laying the groundwork for our investigation. Within the dynamic field of affective computing, the endeavor to devise reliable emotion recognition systems is accelerating, driven by technological advances in sensor capabilities and machine learning methodologies. [PERSON] et al. [27] delineated the shift of emotion recognition efforts from the confines of laboratory environments to real-world applications, underlining the critical role that readily available sensor technologies and advanced signal processing methods play. This shift has been further propelled by innovations in learning algorithms, as expounded by [PERSON] et al. [28], who highlighted the seamless integration of these advancements into digital platforms, thus expanding the horizons of emotion recognition applications. Given the complex nature of human emotions, a multimodal approach is paramount to enhance the accuracy and dependability of emotion recognition systems. [PERSON] et al. [29] offered an extensive analysis of the field of affective computing, emphasizing the amalgamation of behavioral cues with physiological data to discern subtler emotional nuances. Their review not only illuminated the prevailing methodologies, but also paved the way for forthcoming investigative endeavors within this sphere. Echoing the multimodal paradigm, [PERSON] et al.