Tatsuhiro Kimura

A Fundamental Study Toward Development of a New Brain Computer Interface Using a Checker-Board Pattern Reversal Stimulation

The purpose of this study is to investigate the spectral changes of electroencephalogram (EEG) toward development of a new Brain Computer Interface (BCI) for disabled people with verbal communication disorders such as Amyotrophic Lateral Sclerosis (ALS). In this study, an experiment using EEG recordings was carried out in nine healthy adult volunteers. Periodically reversing checker-board stimuli with two kinds of frequencies (5, 15 Hz) were used to observe users’ selective attention from EEG spectral changes. The stimuli were displayed in two different ways, independently displayed and simultaneously displayed, on the LCD of a personal computer. Volunteers were instructed to attend either 5, 15 Hz or neither of the reversing stimulus during EEG recordings. Obtained EEG data were analyzed by FFT and those power spectra were calculated. As a result, two different frequencies reversal stimuli generated peak of EEG spectrum with attended stimulus frequency. However, the peak generated by 5 Hz stimulus was somehow bigger than that of 15 Hz stimulus due to individual differences. To obtain the comparable height of EEG spectral peaks, the compensate procedure to reduce the sensitivity difference between the two frequencies for each person is required. From a comparison of the EEG power spectral structures, subjective binary decision (5 or 15 Hz reversal stimuli) could be discriminated objectively. Utilizing this phenomenon, EEG based BCI for subjective selection extraction can be constructed. Some problems of feasibility of this method as a BCI were also discussed.

Automatic analysis of EEG and behavior records of rats using an FFT

For animal studies with psychotropic drugs, such as sleep-inducer, fundamental data for establishing the characteristics of a standard state of EEG is essential. In this study, statistical analysis using rat biometric system MUPREMS was utilized and polygraphic measurements were carried out over a 24 hour period. Since visually operated classification of recorded data takes a long time, an automated computer analysis system for rat EEG was developed using FFT. EEG, ECG, EMG and EOG were measured from three Wistar rats. Infrared video recordings were also carried out. Four stages from the video recording, six stages from biomedical recordings were defined to determine the sleep stage and wakefulness. Outputs were compared with the classification results of the visual inspection. As a result, this method was comparable to visual classification. The future problem is also found for the state integration such contextual characteristics on the time axis with the behavior data.