第十五期 fNIRS Journal Club 通知 2020/12/27,10am

51 sec read

胡玥

香港中文大学二年级博士生胡玥为大家介绍一篇方法学文献,即基于人工神经网络重構的多通道fNIRS信号运动伪影校正。该方法不仅对心理学,还对运动科学以及康复医学等领域的研究具有重要的参考价值,热烈欢迎大家参与讨论。

时间: 北京时间2020年12月27日周日上午10点
地点: https://zoom.com
房间号: 859 4100 6556
密码: 467563

Lee, G., Jin, S. H., & An, J. (2018). Motion artifact correction of multi-measured functional near-infrared spectroscopy signals based on signal reconstruction using an artificial neural network. Sensors, 18(9), 2957. 点击查看

该文献的PUBMED信息
PMID: 30189651 
PMCID: PMC6164948 
DOI: 10.3390/s18092957

Abstract: In this paper, a new motion artifact correction method is proposed based on multi-channel functional near-infrared spectroscopy (fNIRS) signals. Recently, wavelet transform and hemodynamic response function-based algorithms were proposed as methods of denoising and detrending fNIRS signals. However, these techniques cannot achieve impressive performance in the experimental environment with lots of movement such as gait and rehabilitation tasks because hemodynamic responses have features similar to those of motion artifacts. Moreover, it is difficult to correct motion artifacts in multi-measured fNIRS systems, which have multiple channels and different noise features in each channel. Thus, a new motion artifact correction method for multi-measured fNIRS is proposed in this study, which includes a decision algorithm to determine the most contaminated fNIRS channel based on entropy and a reconstruction algorithm to correct motion artifacts by using a wavelet-decomposed back-propagation neural network. The experimental data was achieved from six subjects and the results were analyzed in comparing conventional algorithms such as HRF smoothing, wavelet denoising, and wavelet MDL. The performance of the proposed method was proven experimentally using the graphical results of the corrected fNIRS signal, CNR that is a performance evaluation index, and the brain activation map.

采用基于频率簇(Cluster)的置换检验(Permutation)方法选取感兴趣频段

作者:北京师范大学 龙宇航,longyuhangwork@163.com代码来源(见本页底部):周思远 在使用wtc计算脑间神经同步后,我们需要在多个频率段、多个通道组合上对神经同步值进行统计检验,因此当进行频段选择时,面临多重比较的问题。为了解决多重比较的问题,可以采取基于参数或非参数检验的多重比较矫正的方法。由于基于非参数检验的多重比较矫正对数据的分布形态没有严格要求,因此具有更广泛的应用场景 (Maris and Oostenveld, 2007)。本文即介绍基于随机置换的非参数检验的方法 (Zheng et al., 2020; Long et al., 2021)。 在寻找感兴趣的效应时,我们采取了基于频率簇(Cluster)的方法,即在频率方向寻找连续显著的Cluster,该方法比基于最强效应点的方法具有更为优秀的抗噪音能力。值得注意的是,我们并没有沿着通道的方向去寻找连续显著的通道簇,这是因为沿着通道方向寻找Cluster容易受到生理噪音的影响。 下面进入具体的实操部分。假设本例招募了22对组1被试及22对组2被试,每对被试分别进行3种条件的任务,因此本例是2(组别,被试间因素)*3(条件,被试内因素)的实验设计。本例对神经同步值进行2*3的混合方差分析,并关注交互作用。 具体来讲,进行置换检验需要进行以下几个步骤:1. 重采样;2. 对随机样本进行计算及统计;3. 计算真实样本的统计量;4. 真实样本与随机样本的对比。下面依次进行介绍。 1. 重采样...
Xu Cui
1 min read

第二十期 fNIRS Journal Club 通知 2021/06/26,10:00am

同时用fNIRS和EEG测量脑信号有哪些好处?技术上应该注意什么?美国斯坦福大学李日辉博士,将为大家讲解他做过的一个同时用fNIRS和EEG测量的实验。热烈欢迎大家参与讨论。 时间: 北京时间2021年6月26日上午10:00地点: https://zoom.com房间号: 856 9352 0230密码: 695930 李博士要讲解的文章如下: Li, Li, Roh, Wang, Zhang (2020) Multimodal Neuroimaging Using Concurrent EEG/fNIRS for Poststroke Recovery Assessment:...
Xu Cui
7 sec read

Calculate phase difference between two general signals (e.g. HbO…

In a recent fNIRS journal club (vedio recorded here), Dr. Tong talked about their work on the phase difference between oxy and deoxy Hb,...
Xu Cui
1 min read

Leave a Reply

Your email address will not be published. Required fields are marked *

Loading