Yusuke Yokota

Activity in the gamma range is related to many sensory and cognitive processes that are impaired in neuropsychiatric conditions. Therefore, individualized measures of gamma-band activity are considered to be potential markers that reflect the state of networks within the brain. Relatively little has been studied in respect of the individual gamma frequency (IGF) parameter. The methodology for determining the IGF is not well established. In the present work, we tested the extraction of IGFs from electroencephalogram (EEG) data in two datasets where subjects received auditory stimulation consisting of clicks with varying inter-click periods, covering a 30–60 Hz range: in 80 young subjects EEG was recorded with 64 gel-based electrodes; in 33 young subjects, EEG was recorded using three active dry electrodes. IGFs were extracted from either fifteen or three electrodes in frontocentral regions by estimating the individual-specific frequency that most consistently exhibited high phase locking during the stimulation. The method showed overall high reliability of extracted IGFs for all extraction approaches; however, averaging over channels resulted in somewhat higher reliability scores. This work demonstrates that the estimation of individual gamma frequency is possible using a limited number of both the gel and dry electrodes from responses to click-based chirp-modulated sounds.

This research aims to design an user interface for controlling a tele-operation robot by brain machine interface (BMI). In this research,we use a BMI based on Steady-State Visual Evoked Potentials (SSVEP). We carried out experiments to compare Support Vector Machine (SVM) and Multivariate Synchronization Index (MSI) for analysing SSVEP. Consequently, SVM achieves higher discrimination accuracy than MSI, and thus we decided to use SVM to analyze SSVEP. Since a BMI can transfer only a small amount of information at a time, we need to design an interface which can get the user’s intention efficiently. For this purpose, we have designed an interface which combines the robot’s scene recognition abilities with a BMI. We carried out tele-operation experiments to validate the effectiveness of the interface.