Hyeong-Yeol Kim

Psychophysiological changes by navigation in a virtual reality

We examined the psychophysiological effects of navigation in a virtual reality (VR). Subjects were exposed to the VR, and required to detect specific objects. Ten electrophysiological signals were recorded before, during, and after navigation in the VR. Six questionnaires on the VR experience were acquired from 45 healthy subjects. There were significant changes between the VR period and the pre-VR control period in several psychophysiological measurements. During the VR period, eye blink, skin conductance level, and alpha frequency of EEG were decreased but gamma wave was increased. Physiological changes associated with cybersickness included increased heart rate, eye blink, skin conductance response, and gamma wave and decreased photoplethysmogram and skin temperature. These results suggest an attentional change during VR navigation and activation of the autonomic nervous system for cybersickness. These findings would enhance our understanding for the psychophysiological changes during VR navigation and cybersickness.

Automatic detection of nausea using bio-signals during immersion in a virtual reality environment

VR (Virtual Reality) systems have been widely used for various purposes. However, during peoples immersion in a virtual environment it is commonly reported that simulation sickness can occur, and it prevents us from utilizing a VR environment for wider purposes. We constructed a controlled VR environment for analyzing the change of bio-signals during VR immersion, where subjects were requested to find trash cans in the virtual environment within five minutes. Each subjects various bio-signals, which were EEGs from 5 different locations, vertical EOG, lead I ECG, fingertip skin temperature, photoplethysmogram, and skin conductance level, were measured during experiments. We analyzed and compared the signals, and we found out that the characteristics of 28 signals during nausea were statistically different from when the subjects were at rest, or during the first 30 seconds after the immersion was started. We parameterized these characteristics and established 12 principal components using principal component analysis in order to reduce the redundancy in those parameters, and constructed an artificial neural network with those principal components. Using the network we constructed, we could partially detect nausea in real time.