Social VR is the application of virtual reality that supports remote social interaction in virtual spaces. Users communicate and interact with others in the social VR environment through avatars, which are virtual anthropomorphic characters that aim to represent humans in virtual worlds. In addition, the development of the HMD and commercially available motion capture systems enable the avatars in the virtual environment to detect and reflect the real-time motions, even facial expressions of people. However, the avatars still lack an indication of biofeedback - e.g., body temperature, breathing, heart rate, muscle contraction -, which serves as social cues for communication in reality. While some features, for example, emojis, supports users to express their feeling or emotions for richer communication, the missing information often results in miscommunication in the virtual space. It remains a barrier to a fully immersed experience in the social VR space. This project proposes a concept of visualizing biosignals of the avatars in the social virtual reality space for a richer-level interaction in virtual reality. With the technologies available to capture and reflect accurate biofeedback in real-time, we would like to explore ways and possibilities to map the bio states of the users in reality to avatars in the virtual world. The project starts with conducting user researches to understand the current user behaviors in the social VR spaces and their perspectives on sharing biosignals. Based on the requirements gathered from the user study, the scope of the project is narrowed down to a ‘watching entertainment’ scenario, and the ways to visualize biosignals on avatars were explored through a co-design session with designers. After that, four biosignal visualization techniques in two biosignals - heart rate and breathing rate - are prototyped under the VR jazz bar setting. Finally, the user study is conducted with 16 pairs (32 participants in total) to test and compare the effects of each biosignal visualization technique in watching entertainment scenarios with a companion. As a result, the embodied visualizations are the most understandable and least distracting visualization method among the four methods. Furthermore, the limitations of the research, recommendations on biosignal visualizations, and recommendations on conducting design research are provided.