Yushin Lee

A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives

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.

The Effects of Curvature of Edge Screen on Subjective Feelings in Smartphone Usage

As the penetration rate of Smartphone grows high and the market gets saturated, the technological and aesthetic progress for manufacturers are heavily demanded to sustain their market share. To meet such demands, manufacturers continuously improve the design of their products. As the design of smartphone varies by manufacturers, there have been many existing researches to identify the relationships between various physical dimensions of smartphones and how users feel (Chowdhury, A., & Kanetkar, M., 2017; Lee, S., Kyung, G., Lee, J., Moon, S. K., & Park, K. J., 2016; Pereira, A., Miller, T., Huang, Y. M., Odell, D., & Rempel, D., 2013; Sung, K., Cho, J., & Freivalds, A., 2016). Recently, in accordance with the development and improvement of flexible displays, curved displays have been applied on smartphones with various curvatures. The popular one among them is the “edge screen” on smartphones, which refers to curved display implemented either on one single side or both sides. Considering that physical dimensions such as shape and size are the important features for customer satisfaction (Hwang, 2012; Ling, C., Hwang, W., and Salvendy, G., 2007), curvature of edge screen can influence on user comfort when the users carry out their tasks on their smartphones. However, such issue hasn’t been dealt in previous studies. Therefore, as a preliminary study, this study aimed to identify the relationships between curvatures of edge screens and subjective feelings in smartphone usage with various usage patterns using subjective evaluation experiment. In this laboratory study, 47 subjects assessed their subjective feelings (grip comfort, control comfort, stability, front visibility, side visibility) towards given samples with four different kinds of usage patterns. The samples were designed with a 5.5-inch flat display of 16:9 ratio (Samsung Galaxy Note 2) as a main screen along with a curved display of different radii of curvatures (R, unit: mm) on the right edge as an edge screen: 4R, 6R, 8R, and 10R. The usage patterns were defined according to which hands were used for grip and control: Grip with right hand and control with right thumb (usage pattern (a)), Grip with left hand and control with right index finger (usage pattern (b)), Grip with both hands and control with both thumbs (usage pattern (c)), and Grip with right hand without control (usage pattern (d)). The statistically significant results of one-way analysis of variance (ANOVA) and post hoc analysis (Duncan’s test) showed that the sample with 4R curvature belonged to the third homogeneous subset (in descending order) for the front visibility while it belonged to the second homogeneous subset for the control comfort in usage pattern (b) and (c). Whereas the sample with 6R curvature belonged to the second homogeneous subset for the front visibility and the second homogeneous subset for the control comfort in usage pattern (b) and (c). In case of the sample with 8R curvature, it was within the first homogeneous subset throughout all analyses conducted. Lastly, the sample with 10R curvature was within the second homogeneous subset for the grip comfort in usage pattern (d). Therefore, it is concluded that the optimal radius of curvature for edge screens is 8R. Although there were some samples evaluated better than the 8R sample in descriptive manner, however, such results were not statistically significant. This study is expected to contribute towards the design of smartphones with edge screens in terms of better subjective feeling by various usage patterns. However, this study is yet a preliminary study which is based on a subjective evaluation. In the future research, more criteria of subjective feeling are needed to be evaluated to decide more precise optimal curvature. In addition, objective evaluation method such as analysis of muscle activity, motion tracking analysis, or behavior analysis could be applied to verify the reliability and validity of the result of this study.

Anthropometric Classification of Human Hand Shapes in Korean Population

Hand tools designed without the anthropometric perspective lead to lower performance and safety. This study suggests a statistical hand shape classification with Korean anthropometric data. A total of 321 anthropometric data were used for this study. To investigate hand types, 27 anthropometric hand variables normalized by the stature of each participant and factor analysis and cluster analysis were conducted. As a result, three major factors were deduced: factors of hand breadth, palm length, and finger length. Additionally, four hand types were determined: (a) a spacious hand with short fingers, (b) a hand with short palm with above average fingers, (c) a long palm and fingers, and (d) a narrow hand and short fingers. In the male population, the spacious hand with short fingers type was dominant while the narrow hand and short fingers type was dominant in the female population. These results are expected to be preliminarily utilized in design for the Korean population.

Deriving the relationship between user satisfaction on engine sounds and affective variable sets based on classification algorithms

This study aims to extract the most relevant set consisted of affective variables to the level of user satisfaction on engine sounds using classification algorithm. The affective variables for engine sounds were defined by three axes, and two classification algorithms were used to determine the prediction accuracy for those affective axes. The study was consisted of three phases: 1) extracting sets of affective variables and the level of satisfaction on engine sounds, 2) preprocessing of engine sounds and experiment design, and 3) analysis of the most relevant sets of affective variables to user satisfaction. As a result, PA (Powerful-Affective) variable set showed the highest prediction accuracy of user satisfaction compared to other sets. Predicting the level of satisfaction based on classification algorithm could help to generalize the relationship between user satisfaction and affective variables more easily, beyond the limitation with a small size of subjects.