###### Abstract  
This paper introduces a novel method for emotion classification within virtual reality (VR) environments, which integrates biosignal processing with advanced machine learning techniques. It focuses on the processing and analysis of electrocardiography (ECG) and galvanic skin response (GSR) signals, which are established indicators of emotional states. To develop a predictive model for emotion classification, we extracted key features, i.e., heart rate variability (HRV), morphological characteristics, and Hjorth parameters. We refined the dataset using a feature selection process based on statistical techniques to optimize it for machine learning applications. The model achieved an accuracy of 97.78% in classifying emotional states, demonstrating that by accurately identifying and responding to user emotions in real time, VR systems can become more immersive, personalized, and emotionally resonant. Ultimately, the potential applications of this method are extensive, spanning various fields. Emotion recognition in education would allow further implementation of adapted learning environments through responding to the current emotional states of students, thereby fostering improved engagement and learning outcomes. The capability for emotion recognition could be used by virtual systems in psychotherapy to provide more personalized and effective therapy through dynamic adjustments of the therapeutic content. Similarly, in the entertainment domain, this approach could be extended to provide the user with a choice regarding emotional preferences for experiences. These applications highlight the revolutionary potential of emotion recognition technology in improving the human-centric nature of digital experiences.  
biosignal analysis; emotion classification; virtual reality; multimodal signal analysis; emotionally adaptive systems; machine learning +
Footnote †: journal:  
emotional states, promises a more natural and continuous recognition of emotions within VR settings [7].  
Our research presents a method that processes and analyzes electrocardiography (ECG) and galvanic skin response (GSR) signals to classify emotions such as excitement, happiness, and anxiety in VR, leveraging the physiological reactions these emotions invoke. The established efficacy of biosignals, with ECG and GSR being the primary indicators of arousal and valence, underpins our approach [8]. We extracted essential features, including heart rate variability (HRV), morphological aspects, and Hjorth parameters. Using a Chi-square-based feature selection in MATLAB, we carefully partitioned our dataset to maintain the integrity of the machine learning process. The application of an ensemble learning algorithm provided robust emotion classification, demonstrating the high accuracy of our method.  
The overarching aim of this research was to forge a method for emotion classification within VR environments using physiological signals. This method strove not only for high accuracy but also for an enhanced user experience through instantaneous emotion detection. Our approach's precision in classifying emotions in VR hints at the transformative potential of biosignal processing coupled with machine learning to redefine VR experiences.  
The remainder of this paper is structured as follows. Section 2 provides background information on existing approaches to emotion classification in VR. Section 3 details our proposed methodology, including the data collection, feature extraction, and classification techniques. Section 4 presents our experimental results and evaluation metrics. Finally, Section 5 discusses the implications of our work and outlines directions for future research.