Kazuki Yanagisawa

Measurement of brain function of car driver using functional near-infrared spectroscopy (fNIRS)

The aim of this study is to propose a method for analyzing measured signal obtained from functional Near-Infrared Spectroscopy (fNIRS), which is applicable for neuroimaging studies for car drivers. We developed a signal processing method by multiresolution analysis (MRA) based on discrete wavelet transform. Statistical group analysis using Z-score is conducted after the extraction of task-related signal using MRA. Brain activities of subjects with different level of mental calculation are measured by fNIRS and fMRI. Results of mental calculation with nine subjects by using fNIRS and fMRI showed that the proposed methods were effective for the evaluation of brain activities due to the task. Finally, the proposed method is applied for evaluating brain function of car driver with and without adaptive cruise control (ACC) system for demonstrating the effectiveness of the proposed method. The results showed that frontal lobe was less active when the subject drove with ACC.

Brain-computer interface using near-infrared spectroscopy for rehabilitation

This study proposes a new method for detecting brain activity level for brain-computer interface (BCI) using a near-infrared spectroscopy (NIRS) which is applicable for rehabilitation. NIRS detects the radiated near-infrared rays, and measures relative variations of oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) based on those absorbencies. The proposed method detects the brain activity level using oxy-Hb and the differential value of oxy-Hb. Results with grasping task show that the proposed method is effective for the detecting of brain activity level.

Measurement of Brain Function Using Near-Infrared Spectroscopy (NIRS)

Near-infrared spectroscopy (NIRS) has gained attention in recent years (Hoshi et al., 2001; Tamura, 2003). This non-invasive technique uses near-infrared light to evaluate increases or decreases in oxygenated hemoglobin or deoxygenated hemoglobin in tissues below the body surface. NIRS can detect the hemodynamics of the brain in real time while the subject is moving. Brain activity can therefore be measured in various environments. Recent research has used NIRS to measure brain activity in a train driver (Kojima et al., 2005, 2006). NIRS has also been used to evaluate the mental activity of an individual driving a car in a driving simulator (Shimizu et al., 2009). Various arguments have focused on interpretation of signals obtained from NIRS, and no uniform signal-processing method has yet been established. Averaging and baseline correction are conventional signal-processing methods used for the NIRS signal. These methods require block design, an experimental technique that involves repeating the same stimuli (tasks) and resting multiple times in order to detect brain activation during a task. However, brain activation has been noted to gradually decline when a subject repeats the same task multiple times (Takahashi et al., 2006). Fourier analysis, which is frequently used for signal analysis, transforms information in the time domain into the frequency domain through the Fourier transform. However, time information is lost in the course of the transform. As the NIRS signal fluctuates, timefrequency analysis is suitable for the NIRS signal. The wavelet transform is an efficient method for time-frequency analysis (Mallat, 1998). This approach adapts the window width in time and frequency so that the window width in frequency becomes smaller when the window width in time is large, or the window width in frequency becomes larger when the window width in time is small. Multi-resolution analysis (MRA) (Mallat, 1989) decomposes the signal into different scales of resolution. MRA with an orthonormal wavelet base effectively facilitates complete decomposition and reconstruction of the signal without losing original information from the signal. Oxygenated hemoglobin and deoxygenated hemoglobin as measured in NIRS are relative values from the beginning of measurement and vary between subjects and parts of the brain. Simple averaging of the NIRS signal thus should not be applied for statistical analysis. To solve this problem, we propose the Z-scored NIRS signal.

NIRS-based neurofeedback learning systems for controlling activity of the prefrontal cortex.

The aim of this study was to develop a NIRS-based neurofeedback system to modulate activity in the prefrontal cortex (PFC). We evaluated the effectiveness of the system in terms of separability of changes in oxy-Hb and its derivative. Training with neurofeedback resulted in higher separability than training without neurofeedback or no training, suggesting that the neurofeedback system could enhance self-control of PFC activity. Interestingly, the dorsolateral PFC exhibited enhanced activity and high separability after neurofeedback training. These observations suggest that the neurofeedback system might be useful for training subjects to regulate emotions by self-control of dorsolateral PFC activity.

Design and implementation of serious games for training and education

Serious games are considered to be effective in the field of training and education to supplement the conventional methods, since their addictivity is effective to keep the motivation of trainees or learners. Although many of serious games have been developed and used, many of them lacks the quantitative analysis of their effectiveness. Moreover, in order that serious games become more popular, an effective software development process which considers the evaluation as an important phase in the process. In this paper, we propose a serious game design process “SGDP” and show the serious games that that applied SGDP. We also discuss the method to evaluate serious games by monitoring brain activities of the players.

Proposal of auxiliary diagnosis index for autism spectrum disorder using near-infrared spectroscopy.

Abstract. Lack of a diagnostic index is a problem that needs to be overcome in the diagnosis of autism spectrum disorder (ASD), because this problem prevents an objective assessment based on biomarkers. This paper describes the development of a diagnostic index for ASD using near-infrared spectroscopy (NIRS). We investigated continuous prefrontal hemodynamic changes depending on reciprocal disposition of working memory and nonworking memory tasks using two-channel NIRS. NIRS signals in the prefrontal cortex were compared between high-functioning ASD subjects (n=11) and typically developed (TD) subjects (n=21). The brain activities of the TD subjects were related to experimental design. These results were not confirmed in brain activities of ASD subjects, although the task performance rate was almost equivalent. The brain activities of TD subjects and ASD subjects were evaluated using a weighted separability (WS) index obtained from the feature phase of oxy-hemoglobin and its differential value. Calculation of the t-test (TD subject versus ASD subject) confirmed that WS was significant. This result showed that the proposed index was useful for evaluation of the brain activity of ASD subjects.

Evaluation of pleasant and unpleasant emotions evoked by visual stimuli using NIRS

A relation between a level of brain activity and pleasant and unpleasant emotions has been studied using Near-infrared Spectroscopy (NIRS). In this study, the relation between the brain activity and the pleasant and unpleasant emotions was evaluated using International Affective Picture System (IAPS) and NIRS. Based on the NIRS recording for 21 participants, the effect of pleasant and unpleasant emotion on the brain activity was measured. The detection of pleasant and unpleasant emotion from NIRS signal was conducted using Neural Network. It was shown that the pleasant and unpleasant emotion can be detected with the accuracy of 96% (the highest) and 75% (average).