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.