Chinese Journal of Lasers, Volume. 48, Issue 19, 1918007(2021)

LSTM-Based Recurrent Neural Network for Noise Suppression in fNIRS Neuroimaging: Network Design and Pilot Validation

Dongyuan Liu1, Yao Zhang1, Yang Liu1, Lu Bai1, Pengrui Zhang1, and Feng Gao1,2、*
Author Affiliations
  • 1College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
  • 2Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin 300072, China
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    References(17)

    [1] Boas D A, Elwell C E, Ferrari M et al. Twenty years of functional near-infrared spectroscopy:introduction for the special issue[J]. NeuroImage, 85, 1-5(2014).

    [3] Duan L, Zhao Z P, Lin Y L et al. Wavelet-based method for removing global physiological noise in functional near-infrared spectroscopy[J]. Biomedical Optics Express, 9, 3805-3820(2018).

    [4] Liu D Y, Wang B Y, Pan T T et al. Toward quantitative near infrared brain functional imaging: lock-in photon counting instrumentation combined with tomographic reconstruction[J]. IEEE Access, 7, 86829-86842(2019).

    [6] Lina J M, Dehaes M, Matteau-Pelletier C et al. Complex wavelets applied to diffuse optical spectroscopy for brain activity detection[J]. Optics Express, 16, 1029-1050(2008).

    [7] Kohno S, Miyai I, Seiyama A et al. Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis[J]. Journal of Biomedical Optics, 12, 062111(2007).

    [8] Virtanen J, Noponen T, Meriläinen P. Comparison of principal and independent component analysis in removing extracerebral interference from near-infrared spectroscopy signals[J]. Journal of Biomedical Optics, 14, 054032(2009).

    [10] Scholkmann F, Kleiser S, Metz A J et al. A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology[J]. NeuroImage, 85, 6-27(2014).

    [11] Liu J, Shahroudy A, Xu D et al. Skeleton-based action recognition using spatio-temporal LSTM network with trust gates[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 3007-3021(2018).

    [12] Guo F Z, Kong J, Jiang M. Action recognition based on adaptive fusion of RGB and skeleton features[J]. Laser & Optoelectronics Progress, 57, 201506(2020).

    [13] Wang B Y, Pan T T, Zhang Y et al. A Kalman-based tomographic scheme for directly reconstructing activation levels of brain function[J]. Optics Express, 27, 3229-3246(2019).

    [14] Strangman G, Franceschini M A, Boas D A. Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters[J]. NeuroImage, 18, 865-879(2003).

    [15] Bonomini V, Zucchelli L, Re R et al. Linear regression models and k-means clustering for statistical analysis of fNIRS data[J]. Biomedical Optics Express, 6, 615-630(2015).

    [16] Sakurada T, Goto A, Tetsuka M et al. Prefrontal activity predicts individual differences in optimal attentional strategy for preventing motor performance decline: a functional near-infrared spectroscopy study[J]. Neurophotonics, 6, 025012(2019).

    [17] Liu Yang, Liu D Y, Zhang Y et al. A portable fNIRS-topography system for BCI applications: full parallel detection and pilot paradigm validation[J]. Chinese Journal of Lasers, 48, 1107001(2021).

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    Dongyuan Liu, Yao Zhang, Yang Liu, Lu Bai, Pengrui Zhang, Feng Gao. LSTM-Based Recurrent Neural Network for Noise Suppression in fNIRS Neuroimaging: Network Design and Pilot Validation[J]. Chinese Journal of Lasers, 2021, 48(19): 1918007

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    Paper Information

    Received: Feb. 2, 2021

    Accepted: Mar. 29, 2021

    Published Online: Sep. 24, 2021

    The Author Email: Gao Feng (gaofeng@tju.edu.cn)

    DOI:10.3788/CJL202148.1918007

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