Deep learning-based motion artifact removal in functional near-infrared spectroscopy

Abstract

. Significance: Functional near-infrared spectroscopy (fNIRS), a well-established neuroimaging technique, enables monitoring cortical activation while subjects are unconstrained. However, motion artifact is a common type of noise that can hamper the interpretation of fNIRS data. Current methods that have been proposed to mitigate motion artifacts in fNIRS data are still dependent on expert-based knowledge and the post hoc tuning of parameters. Aim: Here, we report a deep learning method that aims at motion artifact removal from fNIRS data while being assumption free. To the best of our knowledge, this is the first investigation to report on the use of a denoising autoencoder (DAE) architecture for motion artifact removal. Approach: To facilitate the training of this deep learning architecture, we (i) designed a specific loss function and (ii) generated data to mimic the properties of recorded fNIRS sequences. Results: The DAE model outperformed conventional methods in lowering residual motion artifacts, decreasing mean squared error, and increasing computational efficiency. Conclusion: Overall, this work demonstrates the potential of deep learning models for accurate and fast motion artifact removal in fNIRS data.

Deep learning in fNIRS: a review

“…These loss functions may be modified to implement constraints or regularization, e.g., a linear combination of MSE, variance, and two other metrics for a denoising autoencoder (DAE) in Ref. 34 .…”

“…Another study done by Gao et al. 34 used subjects who were performing a precision cutting surgical task based on the fundamentals of laparoscopic surgery (FLS) program, which required a large range of motion. With a DAE and the process shown in Fig.…”

“…53 In this study, the raw fNIRS time series and the estimated canonical response were used as inputs to the network and the resulting CNR of the DL output was 0.63, outperforming wavelet denoising, which achieved a mean CNR of 0.36. Another study done by Gao et al 34 used subjects who were performing a precision cutting surgical task based on the fundamentals of laparoscopic surgery (FLS) program, which required a large range of motion. With a DAE and the process shown in Fig.…”

Motion artifacts removal and evaluation techniques for functional near-infrared spectroscopy signals: A review

“…Several variations of this method have also appeared in recent years ( Chiarelli et al, 2015 ; Shukla et al, 2018 ; Perpetuini et al, 2021 ). Gao Y. et al (2022) addressed that the tuning of the probability threshold is crucial, and Wei et al (2018) designed a dual-threshold structure to improve the performance of the wavelet-based method.…”

“…The convolution neural network (CNN) was adopted for MA removal in 2022 ( Gao Y. et al, 2022 ; Kim et al, 2022 ). The denoising auto-encoder (DAE) model adopted a serial structure incorporating max-pooling and up-sampling layers ( Gao Y. et al, 2022 ).…”