Real-time motion artifact suppression using convolution neural networks with penalty in fNIRS

• Published in: Frontiers in neuroscience Vol. 18; p. 1432138
• Main Authors: Huang, Ruisen, Hong, Keum-Shik, Bao, Shi-Chun, Gao, Fei
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 06.08.2024
• Subjects: artifact rejection; balloon model; functional near-infrared spectroscopy; neural networks; Neuroscience; wavelet; wavelet; balloon model; artifact rejection; functional near-infrared spectroscopy; neural networks
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2024.1432138

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,  < -3.82,  < 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.