Virtual reality (VR) is receiving attention enough to be considered as its revival age in both industrial and academic field. Since VR systems have various types of interaction with users and new types of interaction are constantly being developed, various studies investigating user experience (UX) of VR systems are continuously needed. However, there is still a lack of research on the taxonomy that can recognize the main characteristics of VR system at a glance by reflecting the influencing factors of UX. Therefore, we collected and reviewed the research related to the UX evaluation of the VR system in order to identify the current research status and to suggest future research direction. To achieve this, a systematic review was conducted on UX studies for VR, and taxonomies of VR system including influencing factors of UX were proposed. A total of 393 unique articles were collected, and 65 articles were selected to be reviewed via Systematic Reviews and Meta-Analyses methodology. The selected articles were analyzed according to predefined taxonomies. As a result, current status of research can be identified base on the proposed taxonomies. Besides, issues related to VR devices and technology, and research method for future research directions can be suggested.

A new Brain-Computer Interface (BCI) technique, which is called a hybrid BCI, has recently been proposed to address the limitations of conventional single BCI system. Although some hybrid BCI studies have shown promising results, the field of hybrid BCI is still in its infancy and there is much to be done. Especially, since the hybrid BCI systems are so complicated and complex, it is difficult to understand the constituent and role of a hybrid BCI system at a glance. Also, the complicated and complex systems make it difficult to evaluate the usability of the systems. We systematically reviewed and analyzed the current state-of-the-art hybrid BCI studies, and proposed a systematic taxonomy for classifying the types of hybrid BCIs with multiple taxonomic criteria. After reviewing 74 journal articles, hybrid BCIs could be categorized with respect to 1) the source of brain signals, 2) the characteristics of the brain signal, and 3) the characteristics of operation in each system. In addition, we exhaustively reviewed recent literature on usability of BCIs. To identify the key evaluation dimensions of usability, we focused on task and measurement characteristics of BCI usability. We classified and summarized 31 BCI usability journal articles according to task characteristics (type and description of task) and measurement characteristics (subjective and objective measures). Afterwards, we proposed usability dimensions for BCI and hybrid BCI systems according to three core-constructs: Satisfaction, effectiveness, and efficiency with recommendations for further research. This paper can help BCI researchers, even those who are new to the field, can easily understand the complex structure of the hybrid systems at a glance. Recommendations for future research can also be helpful in establishing research directions and gaining insight in how to solve ergonomics and HCI design issues surrounding BCI and hybrid BCI systems by usability evaluation.