An Energy Efficient Functional near Infrared Spectroscopy System Employing Spatial Adaptive Sampling Technique

Abstract

Functional near-infrared spectroscopy (fNIRS) is considered as a non-invasive and effective brain-computer interface technology. Wearable high-resolution fNIRS requires a largescale LED array, which consumes a lot of power, and shorten the battery life. This paper proposes a spatial adaptive sampling (SAS) method that can take advantage of the spatial sparsity of fNIRS devices and greatly reduce the power consumption while maintaining high image quality. To improve the performance of the proposed SAS technique, a low power binary neural network (BNN) is proposed to accurately predict the current brain task. And the optimal dynamic LED pattern for each brain task is investigated. The proposed SAS technique is validate through an off-line experiment, it can reduce the power consumption of the LED array by 62.5% compared to not using SAS technology while maintaining a PSNR (Peak Signal to Noise Ratio) of 33 dB.Index Terms-Functional near-infrared spectroscopy (fNIRS), power consumption, binary neural network (BNN), spatial adaptive sampling (SAS)