An EEG-based Approach for Evaluating Graphic Icons from the Perspective of Semantic Distance

Fu-Yin Cherng, Wen-Chieh Lin, Jung-Tai King, Yi-Chen Lee.

ACM Conference on Human Factors in Computing Systems(CHI) 2016 Honorable Mention Award


Graphic icons play an increasingly important role in interface design due to the proliferation of digital devices in recent years. Their ability to express information in a universal fashion allows us to immediately interact with new applications, systems, and devices. Icons can, however, cause user confusion and frustration if designed poorly. Several studies have evaluated icons using behavioral-performance metrics such as reaction time as well as self-report methods. However, determining the usability of icons based on behavioral measures alone is not straightforward, because users’ interpretations of the meaning of icons involve various cognitive processes and perceptual mechanisms. Moreover, these perceptual mechanisms are affected not only by the icons themselves, but by usage scenarios. Thus, we need a means of sensitively and continuously measuring users’ different cognitive processes when they are interacting with icons. In this study, we propose an EEG-based approach to icon evaluation, in which users’ EEG signals are measured in multiple usage scenarios. Based on a combination of EEG and behavioral results, we provide a novel interpretation of the participants’ perception during these tasks, and identify some important implications for icon design.


(a) N1 curves on the FZ channel and (b) N400 curves on the CPZ channel in Exp1.
The 0 ms is the time point of S2 onset time and n is the trial number of each condition.

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