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ORIGINAL RESEARCH article

Front. Neurosci.
Sec. Neuroprosthetics
Volume 18 - 2024 | doi: 10.3389/fnins.2024.1432138

Real-Time Motion Artifact Suppression Using Convolution Neural Networks with Penalty in fNIRS

Provisionally accepted
Ruisen Huang Ruisen Huang 1Keum-Shik Hong Keum-Shik Hong 2,3Shi-Chun Bao Shi-Chun Bao 4*Fei GAO Fei GAO 1,4*
  • 1 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen, China
  • 2 Qingdao University, Qingdao, Shandong Province, China
  • 3 Pusan National University, Busan, Busan, Republic of Korea
  • 4 National innovation center for advanced medical devices, Shenzhen, China

The final, formatted version of the article will be published soon.

    Removing motion artifacts (MAs) from functional near-infrared spectroscopy (fNIRS) signals is crucial in practical applications, but a standard procedure is not available yet. Artificial neural networks have found applications in diverse domains, such as voice and image processing, while their utility in signal processing remains limited. In this work, we introduce an innovative neural network-based approach for online fNIRS signals processing, tailored to individual subjects and requiring minimal prior experimental data. Specifically, this approach employs one-dimensional convolutional neural networks with a penalty network (1DCNNwP), incorporating a moving window and an input data augmentation procedure. In the training process, the neural network is fed with simulated data derived from the balloon model for simulation validation and semi-simulated data for experimental validation, respectively. Visual validation underscores 1DCNNwP's capacity to effectively suppress MAs. Quantitative analysis reveals a remarkable improvement in signal-to-noise ratio by over 11.08 dB, surpassing the existing methods, including the spline-interpolation, wavelet-based, temporal derivative distribution repair with a 1 s moving window, and spline Savitzky-Goaly methods. Contrast-to-noise ratio (CNR) analysis further demonstrated 1DCNNwP's ability to restore or enhance CNRs for motionless signals. In the experiments of eight subjects, our method significantly outperformed the other approaches (except offline TDDR, t < -3.82, p < 0.01). With an average signal processing time of 0.53 ms per sample, 1DCNNwP exhibited strong potential for real-time fNIRS data processing. This novel univariate approach for fNIRS signal processing presents a promising avenue that requires minimal prior experimental data and adapts seamlessly to varying experimental paradigms.

    Keywords: neural networks, functional near-infrared spectroscopy, Artifact rejection, Wavelet, Balloon Model

    Received: 13 May 2024; Accepted: 18 Jul 2024.

    Copyright: © 2024 Huang, Hong, Bao and GAO. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Shi-Chun Bao, National innovation center for advanced medical devices, Shenzhen, China
    Fei GAO, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.