Brain-CAVE Interface Based on Steady-State Visual Evoked Potential

Recently, a new modality of the human-computer interface has been more and more emerging; The Brain-Computer Interface (BCI). The BCI is a communication channel, which enables us to send commands to external devices by using human brain activities (Wolpaw et al., 2002). As one of the remarkable achievements, we can see the report on invasive Brain-Machine Interface (BMI) (Hochberg et al., 2006). Besides the technique of the invasive BMI, there are several types of non-invasive approaches for the brain signal acquisitions; for example, functional magnetic resonance imaging (fMRI), near infrared spectroscopy (NIRS), etc. Non-invasive methods have been noteworthy owing to the recent development of the signal processing method as well as acquisition apparatus. The performance of the BCI system is going to be improved and the applications has been seen; for example, the communication tool for people with disability, virtual reality games, and so on. Among them, the electroencephalography (EEG) has been investigated as one of the candidates for the low-cost and portable BCI system. It is well known that the EEG activities can be detected by the scalp recording, which are typically the order of 5-10 micro volts of potentials. The BCI system can extract the specific temporal and spatial patterns from the brain potentials, and translates them into the commands to control the machine according to the users’ intent. A variety of brain activities has been reported so far in the context of the BCI systems based on EEG signals; for instance, motor related potential (Pfurtscheller & Neuper, 1997), event related P300 evoked potential (Farwell & Donchin, 1988), visual evoked potential (VEP) (Kuroiwa & Celesia, 1981), etc. With such brain activities, many applications have been developed in laboratories such as a virtual keyboard or joystick. However, most of them were studied on the system with the simple visual feedback involving a normal computer monitor. The purpose of this book chapter is to show the technique to realize a BCI system in virtual reality environment and to suggest the possibility of the online control of computergenerated objects. Note that the most advantage of testing the BCI system in virtual reality is that we can easily test and simulate procedures for the BCI applications in reality (Pfurtscheller et al., 2006). Our works are based on the VEP, which is expected to yield high performance BCI systems. In spite of the expected use of the VEP, the BCI system based on such EEG oscillations has never reported in immersive virtual environments.

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