摘要
功能性近红外光谱(fNIRS)具有无创、非电离、适宜的时间/空间分辨率等优点,已逐渐成为传统脑功能成像技术(如核磁共振成像、脑电图等)的重要补充,越来越多地被应用于脑功能临床研究。然而,在实际应用中,生理干扰(心跳、呼吸和低频振荡等)和随机噪声(散弹噪声和环境噪声等)往往会给fNIRS脑功能成像带来明显的伪影,甚至"湮灭"真实的大脑兴奋信号。为解决这一问题,本文提出了一种基于长短期记忆(LSTM)循环神经网络的滤波模型,采用具有预测和分类功能的复合神经网络分别抑制生理干扰和随机噪声。本文基于fNIRS-扩散光学层析成像方案开展了数值模拟和在体实验,详细描述了网络设计、训练和滤波过程,并将结果与自适应滤波、多周期平均方法进行对比。结果表明,所提LSTM模型可以有效抑制生理干扰和随机噪声,且无需重复测量即可实现较高的重建质量,为基于fNIRS的脑机接口应用提供了一种有效的技术手段。
Objective Functional near-infrared spectroscopy(fNIRS)has several advantages,such as noninvasiveness,free radiation,and reasonable temporal/spatial resolution.This enables fNIRS-based technologies to be used as an alternative to conventional technologies,such as functional magnetic response imaging(fMRI)and electroencephalogram(EEG),and the technologies are increasingly used in clinical practice to complete neuroimaging.However,because of the reflection geometry used in fNIRS,light travels from a source,through the scalp-skull layer,into the brain,and back out through the scalp-skull layer to be measured by a detector,which decays significantly as depth increases.Therefore,the reconstructed activation using fNIRS is usually contaminated by superficial physiological signals(cardiac pulsation,respiration,and low-frequency oscillations,etc.).Besides,the random interferences induced by the photon-shot and instrumental noises,etc.,also have blurring effects on the activation reconstruction because of faint activated hemodynamics in the brain.Thus,suppressing the irritating physiological interferences and random noises has been a critical task in fNIRS-based neuroimaging.In this work,we propose a long-short-term-memory(LSTM)based recurrent neural network(RNN),including aprediction and a classification layer,to suppress physiological interferences and random noises,respectively,to improve reconstruction performance with less repetitive or even individual stimulation.This has some advantages,including shorter measurement time,more subjects,and the ability to examine responses to single stimulation.Methods The proposed LSTM-based RNN,which is purely data-driven without an auxiliary measurement process,comprises two layers:First,the prediction layer is used to estimate the absorption perturbation induced by the physiological interferences during task stimulation.Then,the estimated time series is used as the reference to adaptively filter the reconstructed absorption perturbation for the removal of the interferences from th
作者
刘东远
张耀
刘洋
白璐
张鹏睿
高峰
Liu Dongyuan;Zhang Yao;Liu Yang;Bai Lu;Zhang Pengrui;Gao Feng(College of Precision Instruments and Optoelectronics Engineering,Tianjin University,Tianjin 300072,China;Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments,Tianjin 300072,China)
出处
《中国激光》
EI
CAS
CSCD
北大核心
2021年第19期306-315,共10页
Chinese Journal of Lasers
基金
国家自然科学基金(81871393,62075156,61575140)。
关键词
医用光学
功能性近红外光谱
长短期记忆
循环神经网络
脑机接口
medical optics
functional near-infrared spectroscopy
long-short-term-memory
recurrent neural network
brain-computer interface
