We present a high spatiotemporal resolution system for neuroimaging using functional near-infrared spectroscopy (fNIRS). The system is configured as bundled optodes with a single photodiode (PD) and 128 dual-wavelength LEDs in a module. This system is developed using a modular approach where a single module can cover approximately 7 cm × 7 cm, while multiple modules can be used to a broader area. The system has the capacity to measure concentration changes of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) at different brain depths ranging from 2 cm to 3.5 cm. The system also provides the superficial layer information by measuring the short-separation channels. The short-separation channels allow removal of noise and enhancement of signals. The drive circuit of LEDs is carefully designed to switch the light with appropriate intensity, which provides a stable reception for each channel. MOSFET based switching is implemented that allows sharp current switching for high-speed data acquisition. The system can display the acquired HbO and HbR signals as well as activation maps in real-time on a lab-developed Windows-based software. The hardware connects to the software using Wi-Fi. Phantom model with known optical properties and a human subject were used for testing the functionality and efficacy of the device. A complete 128 channel fNIRS sample was recorded in 25 ms. The phantom results showed reduced signal intensity when the channel separation was increased that provides the HbO and HbR. The activation was seen using HbO in the human subject while performing hand tapping task.
In this paper, the effect of various channel selection strategies on the initial dip phase of the hemodynamic response (HR) using functional near-infrared spectroscopy (fNIRS) is investigated. The strategies using channel averaging, channel averaging over a local region, t-value-based channel selection, baseline correction, and vector phase analysis are examined. For t-value-based channel selection, three gamma functions are used to model the initial dip, the main HR, and the undershoot in generating the designed HR function. The linear discriminant analysis based classification accuracy is used as performance evaluation criteria. fNIRS signals are obtained from the left motor cortex during righthand thumb and little finger tapping tasks. In classifying two finger tapping tasks, signal mean and minimum value during 0~2.5 sec, as features of initial dip, are used. The results show that the active channel selected using t-value and vector phase analysis yielded the highest averaged classification accuracy. It is also found that the initial dip in the HR disappears in case of averaging overall channels. The results demonstrated the importance of the channel selection in improving the classification accuracy for fNIRS-based brain-computer interface applications. Furthermore, the use of three gamma functions can also be useful for fNIRS brain imaging for detecting the initial dip in the HR.