An Energy-Efficient FD-fNIRS Readout Circuit Employing a Mixer-First Analog Frontend and a Σ-Δ Phase-to-Digital Converter.

This paper presents a low-power frequency-domain functional near-infrared spectroscopy (FD-fNIRS) readout circuit for the absolute value measurement of tissue optical characteristics. The paper proposes a mixer-first analog front-end (AFE) structure and a 1-bit Σ-Δ phase-to-digital converter (PDC) to reduce the required circuit bandwidth and the laser modulation frequency, thereby saving power while maintaining high resolution. The proposed chip achieves sub-0.01° phase resolution and consumes 6.8 mW of power. Nine optical solid phantoms are produced to evaluate the chip. Compared to a self-built high-precision measurement platform that combines a network analyzer with an avalanche photodiode (APD) module, the maximum measuring errors of the absorption coefficient and reduced scattering coefficient are 10.6% and 12.3%, respectively.

An Energy-Efficient Wearable Functional Near-infrared Spectroscopy System Employing Dual-level Adaptive Sampling Technique.

Functional near-infrared spectroscopy (fNIRS) is a powerful medical imaging tool in brain science and psychology, it can also be employed in brain-computer interface (BCI) due to its noninvasive and artifact-less-sensitive characteristics. Conventional ways to detect large-area brain activity using near-infrared (NIR) technology are based on Time-division or Frequency-division modulation technique, which traverses all physical sensory channels in a specific period. To achieve higher imaging resolution or brain-tasks classification accuracy, the NIRS system require higher density and more channels, which conflict with the limited battery capacity. Inspired by the functional atlas of the human brain, this paper proposes a spatial adaptive sampling (SAS) method. It can change the active channel pattern of the fNIRS system to match with the real-time brain activity, to increase the energy efficiency without significant reduction on the brain imaging quality or the accuracy of brain activity classification. Therefore, the number of the averaging enabled channels will be dramatically reduced in practice. To verify the proposed SAS technique, a wearable and flexible NIRS system has been implemented, in which each channel of light-emitting diode (LED) drive circuits and photodiode (PD) detection circuits can be power gated independently. Brain task experiments have been conducted to validate the proposed method, the power consumption of the LED drive module is reduced by 46.58% compared to that without SAS technology while maintaining an average brain imaging PSNR (Peak Signal to Noise Ratio) of 35 dB. The brain-task classification accuracy is 80.47%, which has a 2.67% reduction compared to that without the SAS technique.