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.

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In this paper, we reviewed both studies on general smart car technologies and HCI/HVI studies that were published in journals and conferences, so that we can identify the current status of research and suggest future research directions. Furthermore, we reviewed previous studies on elderly drivers as they could be the most vulnerable social group in terms of new technology acceptance. A total of 257 articles for HCI research and 45 articles for elderly drivers were selected and reviewed from 11,267 collected articles (from 2010 to 2014). According to the results, most articles were mainly related to safety and adaptive features (e.g., driver's state recognition, vehicle surrounding monitoring, driver action-suggestion), and infotainment research in terms of HCI (e.g., IT devices-vehicle interaction, vehicle-vehicle interaction) was relatively insufficient despite its high research demand. According to the results of the literature Downloaded by [New York University] at 03:59 29 July 2015 A c c e p t e d M a n u s c r i p t 2 review and technological trends analysis based on previous technical roadmaps, from HCI/HFE perspectives, research related to 'Assistance systems', 'Physiological & mental state recognition', 'Position sensor technology', 'Behavior recognition', and 'Infotainment' were suggested to HCI/HFE researchers for the further research. In particular, HCI/HFE researchers need to focus on research on acceptable levels of automation, observing new driving behaviors, investigation of driver characteristics to develop personalized services, and new technology acceptance to develop and improve smart cars in the future.