Motion Artifact Removal in Functional Near‐Infrared Spectroscopy Based on Long Short‐Term Memory‐Autoencoder Model
ABSTRACT Motion artifact removal is a critical issue in functional near‐infrared spectroscopy (fNIRS) analysis tasks, with traditional methods relying heavily on expert‐based knowledge and optimal selection of model parameters within brain regions. In this paper, we propose a deep learning denoising...
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| Veröffentlicht in: | The European journal of neuroscience Jg. 61; H. 2 |
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| Abstract | ABSTRACT
Motion artifact removal is a critical issue in functional near‐infrared spectroscopy (fNIRS) analysis tasks, with traditional methods relying heavily on expert‐based knowledge and optimal selection of model parameters within brain regions. In this paper, we propose a deep learning denoising model based on long short‐term memory (LSTM)‐autoencoder (viz., LSTM‐AE) to reduce motion artifacts. By training a neural network to reconstruct hemodynamic response coupled with neuronal activity, LSTM‐AE achieves positive denoising results on both our synthesized noisy simulated dataset and the real dataset. The LSTM‐AE processes the raw fNIRS in three phases: (1) Morphological feature extraction of the raw fNIRS is conducted through the encoder module. (2) The LSTM module captures temporal correlations between individual samples to enhance features. (3) The decoder module recovers and reconstructs the morphological feature information of fNIRS from the latent space. Finally, clean reconstructed fNIRS is generated at the output layer. We compare our proposed method with existing calibration algorithms for hemodynamic response estimation using the following metrics: mean square error (MSE), Pearson's correlation (R2), signal‐to‐noise ratio (SNR), and percent deviation ratio (PDR). The proposed LSTM‐AE method outperforms conventional methods, demonstrating an improvement in all these metrics. Additionally, the proposed LSTM‐AE method shows statistically significant differences from other motion artifact algorithms in terms of effectiveness (p < 0.01, significance level α = 0.05). This study demonstrates the potential of deep network architectures to remove motion artifacts in fNIRS data.
We propose a deep learning denoising model based on long short‐term memory (LSTM)‐autoencoder (viz., LSTM‐AE) to reduce motion artifacts. By training a neural network to reconstruct hemodynamic response coupled with neuronal activity, LSTM‐AE achieves positive denoising results on both our synthesized noisy simulated dataset and the real dataset. This study demonstrates the potential of deep network architectures to remove motion artifacts in fNIRS data. |
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| AbstractList | Motion artifact removal is a critical issue in functional near‐infrared spectroscopy (fNIRS) analysis tasks, with traditional methods relying heavily on expert‐based knowledge and optimal selection of model parameters within brain regions. In this paper, we propose a deep learning denoising model based on long short‐term memory (LSTM)‐autoencoder (viz., LSTM‐AE) to reduce motion artifacts. By training a neural network to reconstruct hemodynamic response coupled with neuronal activity, LSTM‐AE achieves positive denoising results on both our synthesized noisy simulated dataset and the real dataset. The LSTM‐AE processes the raw fNIRS in three phases: (1) Morphological feature extraction of the raw fNIRS is conducted through the encoder module. (2) The LSTM module captures temporal correlations between individual samples to enhance features. (3) The decoder module recovers and reconstructs the morphological feature information of fNIRS from the latent space. Finally, clean reconstructed fNIRS is generated at the output layer. We compare our proposed method with existing calibration algorithms for hemodynamic response estimation using the following metrics: mean square error (MSE), Pearson's correlation (R2), signal‐to‐noise ratio (SNR), and percent deviation ratio (PDR). The proposed LSTM‐AE method outperforms conventional methods, demonstrating an improvement in all these metrics. Additionally, the proposed LSTM‐AE method shows statistically significant differences from other motion artifact algorithms in terms of effectiveness (p < 0.01, significance level α = 0.05). This study demonstrates the potential of deep network architectures to remove motion artifacts in fNIRS data. ABSTRACT Motion artifact removal is a critical issue in functional near‐infrared spectroscopy (fNIRS) analysis tasks, with traditional methods relying heavily on expert‐based knowledge and optimal selection of model parameters within brain regions. In this paper, we propose a deep learning denoising model based on long short‐term memory (LSTM)‐autoencoder (viz., LSTM‐AE) to reduce motion artifacts. By training a neural network to reconstruct hemodynamic response coupled with neuronal activity, LSTM‐AE achieves positive denoising results on both our synthesized noisy simulated dataset and the real dataset. The LSTM‐AE processes the raw fNIRS in three phases: (1) Morphological feature extraction of the raw fNIRS is conducted through the encoder module. (2) The LSTM module captures temporal correlations between individual samples to enhance features. (3) The decoder module recovers and reconstructs the morphological feature information of fNIRS from the latent space. Finally, clean reconstructed fNIRS is generated at the output layer. We compare our proposed method with existing calibration algorithms for hemodynamic response estimation using the following metrics: mean square error (MSE), Pearson's correlation (R2), signal‐to‐noise ratio (SNR), and percent deviation ratio (PDR). The proposed LSTM‐AE method outperforms conventional methods, demonstrating an improvement in all these metrics. Additionally, the proposed LSTM‐AE method shows statistically significant differences from other motion artifact algorithms in terms of effectiveness (p < 0.01, significance level α = 0.05). This study demonstrates the potential of deep network architectures to remove motion artifacts in fNIRS data. We propose a deep learning denoising model based on long short‐term memory (LSTM)‐autoencoder (viz., LSTM‐AE) to reduce motion artifacts. By training a neural network to reconstruct hemodynamic response coupled with neuronal activity, LSTM‐AE achieves positive denoising results on both our synthesized noisy simulated dataset and the real dataset. This study demonstrates the potential of deep network architectures to remove motion artifacts in fNIRS data. Motion artifact removal is a critical issue in functional near‐infrared spectroscopy (fNIRS) analysis tasks, with traditional methods relying heavily on expert‐based knowledge and optimal selection of model parameters within brain regions. In this paper, we propose a deep learning denoising model based on long short‐term memory (LSTM)‐autoencoder (viz., LSTM‐AE) to reduce motion artifacts. By training a neural network to reconstruct hemodynamic response coupled with neuronal activity, LSTM‐AE achieves positive denoising results on both our synthesized noisy simulated dataset and the real dataset. The LSTM‐AE processes the raw fNIRS in three phases: (1) Morphological feature extraction of the raw fNIRS is conducted through the encoder module. (2) The LSTM module captures temporal correlations between individual samples to enhance features. (3) The decoder module recovers and reconstructs the morphological feature information of fNIRS from the latent space. Finally, clean reconstructed fNIRS is generated at the output layer. We compare our proposed method with existing calibration algorithms for hemodynamic response estimation using the following metrics: mean square error (MSE), Pearson's correlation ( R 2 ), signal‐to‐noise ratio (SNR), and percent deviation ratio (PDR). The proposed LSTM‐AE method outperforms conventional methods, demonstrating an improvement in all these metrics. Additionally, the proposed LSTM‐AE method shows statistically significant differences from other motion artifact algorithms in terms of effectiveness ( p < 0.01, significance level α = 0.05). This study demonstrates the potential of deep network architectures to remove motion artifacts in fNIRS data. |
| Author | Wang, Junhong Yang, Pan Xi, Xugang Wang, Ting Xu, Dongjuan Li, Lihua |
| Author_xml | – sequence: 1 givenname: Pan surname: Yang fullname: Yang, Pan organization: Hangzhou Dianzi University – sequence: 2 givenname: Junhong surname: Wang fullname: Wang, Junhong organization: Hangzhou Dianzi University – sequence: 3 givenname: Ting orcidid: 0000-0003-3000-5315 surname: Wang fullname: Wang, Ting organization: Hangzhou Dianzi University – sequence: 4 givenname: Lihua surname: Li fullname: Li, Lihua organization: Hangzhou Dianzi University – sequence: 5 givenname: Dongjuan surname: Xu fullname: Xu, Dongjuan email: xdj0108@wmu.edu.cn organization: Affiliated Dongyang Hospital of Wenzhou Medical University – sequence: 6 givenname: Xugang surname: Xi fullname: Xi, Xugang email: xixi@hdu.edu.cn organization: Hangzhou Dianzi University |
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| Cites_doi | 10.1016/j.neuroimage.2013.11.033 10.1142/S1793545813500661 10.1117/1.NPh.9.4.041406 10.1016/S0166-2236(97)01132-6 10.1109/JBHI.2022.3227320 10.3390/s18092957 10.1021/ac60319a045 10.1109/TNSRE.2022.3201197 10.1088/1741-2552/aaaf82 10.1109/TNNLS.2023.3293328 10.1088/0031-9155/33/12/008 10.1161/01.CIR.101.23.e215 10.1088/0967-3334/31/5/004 10.1117/1.NPh.3.3.031410 10.1016/j.neuroimage.2015.02.057 10.1016/j.media.2019.101622 10.1117/1.1852552 10.3390/s23083979 10.3389/fnhum.2020.00030 10.1007/s11571-023-09986-4 10.1109/BIBM.2018.8621080 10.1016/j.neuroimage.2009.11.050 10.1088/0967-3334/33/2/259 10.3389/fnins.2012.00147 10.1364/AO.48.00D280 10.1109/TITB.2012.2207400 10.1371/journal.pone.0244186 10.1016/j.neuroimage.2013.06.054 10.1201/9781420038491.ch8 10.1002/hbm.20767 10.1007/BF02447083 10.1016/j.neuroimage.2018.09.025 10.1080/01621459.1979.10481038 10.1117/1.NPh.5.1.015003 10.1038/s41598-023-47812-3 10.1364/BOE.4.001366 10.1016/j.patcog.2023.109603 10.1109/EMBC46164.2021.9631014 10.1109/TFUZZ.2021.3062723 |
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| Copyright | 2025 Federation of European Neuroscience Societies and John Wiley & Sons Ltd. 2025 Federation of European Neuroscience Societies and John Wiley & Sons Ltd |
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| Notes | Edited by Funding This work was supported by the National Natural Science Foundation of China (Nos. 62371178 and 62301197), Zhejiang Provincial Key Research and Development Program of China (No. 2024C03041), and Zhejiang Provincial Natural Science Foundation of China (No. LTGY23H180020). John Foxe ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
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| References | 2014b; 85 2010; 31 2023; 13 2013; 4 2023; 141 1997; 20 2020; 60 1988; 33 2020; 15 2020; 14 2022b; 9 2012; 16 2002 2024 2024; 35 2014; 85 2019; 184 1979; 74 2021; 30 2012; 33 1972; 44 2024; 18 2009; 48 2018; 18 2010; 49 2009; 30 2018; 5 2023; 23 2016; 3 2021 2015; 112 1988; 26 2018 2005; 10 2022; 30 2000; 101 2012; 6 2018; 15 2022a; 27 2014a; 7 e_1_2_12_4_1 e_1_2_12_3_1 e_1_2_12_6_1 e_1_2_12_5_1 e_1_2_12_19_1 e_1_2_12_18_1 e_1_2_12_2_1 e_1_2_12_17_1 e_1_2_12_16_1 e_1_2_12_38_1 e_1_2_12_39_1 e_1_2_12_20_1 e_1_2_12_41_1 e_1_2_12_21_1 e_1_2_12_22_1 e_1_2_12_23_1 e_1_2_12_24_1 e_1_2_12_25_1 e_1_2_12_26_1 e_1_2_12_40_1 e_1_2_12_27_1 e_1_2_12_28_1 e_1_2_12_29_1 e_1_2_12_30_1 e_1_2_12_31_1 e_1_2_12_32_1 e_1_2_12_33_1 e_1_2_12_34_1 e_1_2_12_35_1 e_1_2_12_36_1 e_1_2_12_37_1 e_1_2_12_15_1 e_1_2_12_14_1 e_1_2_12_13_1 e_1_2_12_12_1 e_1_2_12_8_1 e_1_2_12_11_1 e_1_2_12_7_1 e_1_2_12_10_1 e_1_2_12_9_1 |
| References_xml | – volume: 10 issue: 1 year: 2005 article-title: Eigenvector‐Based Spatial Filtering for Reduction of Physiological Interference in Diffuse Optical Imaging publication-title: Journal of Biomedical Optics – volume: 101 start-page: e215 issue: 23 year: 2000 end-page: e220 article-title: PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals publication-title: Circulation – start-page: 2605 year: 2018 end-page: 2612 – volume: 33 start-page: 259 issue: 2 year: 2012 end-page: 270 article-title: Wavelet‐Based Motion Artifact Removal for Functional Near‐Infrared Spectroscopy publication-title: Physiological Measurement – volume: 4 start-page: 1366 issue: 8 year: 2013 end-page: 1379 article-title: Autoregressive Model Based Algorithm for Correcting Motion and Serially Correlated Errors in fNIRS publication-title: Biomedical Optics Express – volume: 184 start-page: 171 year: 2019 end-page: 179 article-title: Temporal Derivative Distribution Repair (TDDR): A Motion Correction Method for fNIRS publication-title: NeuroImage – volume: 74 start-page: 829 issue: 368 year: 1979 end-page: 836 article-title: Robust Locally Weighted Regression and Smoothing Scatterplots publication-title: Journal of the American Statistical Association – volume: 30 start-page: 3426 issue: 10 year: 2009 end-page: 3435 article-title: The fMRI Success Rate of Children and Adolescents: Typical Development, Epilepsy, Attention Deficit/Hyperactivity Disorder, and Autism Spectrum Disorders publication-title: Human Brain Mapping – volume: 5 issue: 1 year: 2018 article-title: Motion Artifact Detection and Correction in Functional Near‐Infrared Spectroscopy: A New Hybrid Method Based on Spline Interpolation Method and Savitzky‐Golay Filtering publication-title: Neurophotonics – start-page: 828 year: 2021 end-page: 831 – year: 2024 – start-page: 193 year: 2002 end-page: 221 – volume: 15 issue: 12 year: 2020 article-title: Comparing fNIRS Signal Qualities Between Approaches With and Without Short Channels publication-title: PLoS ONE – volume: 16 start-page: 918 issue: 5 year: 2012 end-page: 926 article-title: A Methodology for Validating Artifact Removal Techniques for Physiological Signals publication-title: IEEE Transactions on Information Technology in Biomedicine – volume: 141 year: 2023 article-title: Multi Scale Pixel Attention and Feature Extraction Based Neural Network for Image Denoising publication-title: Pattern Recognition – volume: 15 issue: 3 year: 2018 article-title: Deep Learning for Hybrid EEG‐fNIRS Brain–Computer Interface: Application to Motor Imagery Classification publication-title: Journal of Neural Engineering – volume: 31 start-page: 649 issue: 5 year: 2010 end-page: 662 article-title: How to Detect and Reduce Movement Artifacts in Near‐Infrared Imaging Using Moving Standard Deviation and Spline Interpolation publication-title: Physiological Measurement – volume: 49 start-page: 3039 issue: 4 year: 2010 end-page: 3046 article-title: Functional Near Infrared Spectroscopy (NIRS) Signal Improvement Based on Negative Correlation Between Oxygenated and Deoxygenated Hemoglobin Dynamics publication-title: NeuroImage – volume: 48 start-page: D280 issue: 10 year: 2009 end-page: D298 article-title: HomER: A Review of Time‐Series Analysis Methods for Near‐Infrared Spectroscopy of the Brain publication-title: Applied Optics – volume: 18 issue: 9 year: 2018 article-title: Motion Artifact Correction of Multi‐Measured Functional Near‐Infrared Spectroscopy Signals Based on Signal Reconstruction Using an Artificial Neural Network publication-title: Sensors – volume: 33 start-page: 1433 issue: 12 year: 1988 end-page: 1442 article-title: Estimation of Optical Pathlength Through Tissue From Direct Time of Flight Measurement publication-title: Physics in Medicine & Biology – volume: 20 start-page: 435 issue: 10 year: 1997 end-page: 442 article-title: Non‐invasive Optical Spectroscopy and Imaging of Human Brain Function publication-title: Trends in Neurosciences – volume: 27 start-page: 1283 issue: 3 year: 2022a end-page: 1294 article-title: EEG Reconstruction With a Dual‐Scale CNN‐LSTM Model for Deep Artifact Removal publication-title: IEEE Journal of Biomedical and Health Informatics – volume: 35 start-page: 16262 issue: 11 year: 2024 end-page: 16276 article-title: Eigenimage2Eigenimage (E2E): A Self‐Supervised Deep Learning Network for Hyperspectral Image Denoising publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 30 start-page: 2474 year: 2022 end-page: 2485 article-title: Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One‐Dimensional CNN AutoEncoder publication-title: IEEE Transactions on Neural Systems and Rehabilitation Engineering – volume: 7 issue: 02 year: 2014a article-title: Targeted Principle Component Analysis: A new Motion Artifact Correction Approach for Near‐Infrared Spectroscopy publication-title: Journal of Innovative Optical Health Sciences – volume: 23 issue: 8 year: 2023 article-title: Improved Motion Artifact Correction in fNIRS Data by Combining Wavelet and Correlation‐Based Signal Improvement publication-title: Sensors – volume: 26 start-page: 289 year: 1988 end-page: 294 article-title: System for Long‐Term Measurement of Cerebral Blood and Tissue Oxygenation on Newborn Infants by Near Infra‐Red Transillumination publication-title: Medical and Biological Engineering and Computing – volume: 44 start-page: 1906 issue: 11 year: 1972 end-page: 1909 article-title: Smoothing and Differentiation of Data by Simplified Least Square Procedure publication-title: Analytical Chemistry – volume: 14 start-page: 30 year: 2020 article-title: Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective publication-title: Frontiers in Human Neuroscience – volume: 13 issue: 1 year: 2023 article-title: Fuzzy Inference‐Based LSTM for Long‐Term Time Series Prediction publication-title: Scientific Reports – volume: 60 year: 2020 article-title: A Robust Deep Neural Network for Denoising Task‐Based fMRI Data: An Application to Working Memory and Episodic Memory publication-title: Medical Image Analysis – volume: 85 start-page: 1 year: 2014 end-page: 5 article-title: Twenty Years of Functional Near‐Infrared Spectroscopy: Introduction for the Special Issue publication-title: NeuroImage – volume: 6 year: 2012 article-title: A Systematic Comparison of Motion Artifact Correction Techniques for Functional Near‐Infrared Spectroscopy publication-title: Frontiers in Neuroscience – volume: 30 start-page: 1599 issue: 6 year: 2021 end-page: 1613 article-title: Building Trend Fuzzy Granulation‐Based LSTM Recurrent Neural Network for Long‐Term Time‐Series Forecasting publication-title: IEEE Transactions on Fuzzy Systems – volume: 85 start-page: 192 year: 2014b end-page: 201 article-title: Reducing Motion Artifacts for Long‐Term Clinical NIRS Monitoring Using Collodion‐Fixed Prism‐Based Optical Fibers publication-title: NeuroImage – volume: 112 start-page: 128 year: 2015 end-page: 137 article-title: A Kurtosis‐Based Wavelet Algorithm for Motion Artifact Correction of fNIRS Data publication-title: NeuroImage – volume: 18 start-page: 1489 issue: 4 year: 2024 end-page: 1506 article-title: Deep Learning Networks Based Decision Fusion Model of EEG and fNIRS for Classification of Cognitive Tasks publication-title: Cognitive Neurodynamics – volume: 3 issue: 3 year: 2016 article-title: Correction of Motion Artifacts and Serial Correlations for Real‐Time Functional Near‐Infrared Spectroscopy publication-title: Neurophotonics – volume: 9 issue: 4 year: 2022b article-title: Deep Learning‐Based Motion Artifact Removal in Functional Near‐Infrared Spectroscopy publication-title: Neurophotonics – ident: e_1_2_12_5_1 doi: 10.1016/j.neuroimage.2013.11.033 – ident: e_1_2_12_38_1 doi: 10.1142/S1793545813500661 – ident: e_1_2_12_16_1 doi: 10.1117/1.NPh.9.4.041406 – ident: e_1_2_12_32_1 doi: 10.1016/S0166-2236(97)01132-6 – ident: e_1_2_12_15_1 doi: 10.1109/JBHI.2022.3227320 – ident: e_1_2_12_22_1 doi: 10.3390/s18092957 – ident: e_1_2_12_28_1 doi: 10.1021/ac60319a045 – ident: e_1_2_12_21_1 – ident: e_1_2_12_25_1 doi: 10.1109/TNSRE.2022.3201197 – ident: e_1_2_12_7_1 doi: 10.1088/1741-2552/aaaf82 – ident: e_1_2_12_41_1 doi: 10.1109/TNNLS.2023.3293328 – ident: e_1_2_12_13_1 doi: 10.1088/0031-9155/33/12/008 – ident: e_1_2_12_18_1 doi: 10.1161/01.CIR.101.23.e215 – ident: e_1_2_12_27_1 doi: 10.1088/0967-3334/31/5/004 – ident: e_1_2_12_4_1 doi: 10.1117/1.NPh.3.3.031410 – ident: e_1_2_12_8_1 doi: 10.1016/j.neuroimage.2015.02.057 – ident: e_1_2_12_35_1 doi: 10.1016/j.media.2019.101622 – ident: e_1_2_12_39_1 doi: 10.1117/1.1852552 – ident: e_1_2_12_2_1 doi: 10.3390/s23083979 – ident: e_1_2_12_33_1 doi: 10.3389/fnhum.2020.00030 – ident: e_1_2_12_26_1 doi: 10.1007/s11571-023-09986-4 – ident: e_1_2_12_23_1 doi: 10.1109/BIBM.2018.8621080 – ident: e_1_2_12_12_1 doi: 10.1016/j.neuroimage.2009.11.050 – ident: e_1_2_12_24_1 doi: 10.1088/0967-3334/33/2/259 – ident: e_1_2_12_10_1 doi: 10.3389/fnins.2012.00147 – ident: e_1_2_12_19_1 doi: 10.1364/AO.48.00D280 – ident: e_1_2_12_29_1 doi: 10.1109/TITB.2012.2207400 – ident: e_1_2_12_40_1 doi: 10.1371/journal.pone.0244186 – ident: e_1_2_12_37_1 doi: 10.1016/j.neuroimage.2013.06.054 – ident: e_1_2_12_6_1 doi: 10.1201/9781420038491.ch8 – ident: e_1_2_12_36_1 doi: 10.1002/hbm.20767 – ident: e_1_2_12_11_1 doi: 10.1007/BF02447083 – ident: e_1_2_12_14_1 doi: 10.1016/j.neuroimage.2018.09.025 – ident: e_1_2_12_9_1 doi: 10.1080/01621459.1979.10481038 – ident: e_1_2_12_20_1 doi: 10.1117/1.NPh.5.1.015003 – ident: e_1_2_12_34_1 doi: 10.1038/s41598-023-47812-3 – ident: e_1_2_12_3_1 doi: 10.1364/BOE.4.001366 – ident: e_1_2_12_31_1 doi: 10.1016/j.patcog.2023.109603 – ident: e_1_2_12_17_1 doi: 10.1109/EMBC46164.2021.9631014 – ident: e_1_2_12_30_1 doi: 10.1109/TFUZZ.2021.3062723 |
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Motion artifact removal is a critical issue in functional near‐infrared spectroscopy (fNIRS) analysis tasks, with traditional methods relying heavily... Motion artifact removal is a critical issue in functional near‐infrared spectroscopy (fNIRS) analysis tasks, with traditional methods relying heavily on... |
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| SubjectTerms | Algorithms Deep learning functional near‐infrared spectroscopy Hemodynamics Infrared spectroscopy LSTM‐AE Morphology motion Neural networks reconstruct hemodynamic response Spectrum analysis Statistical analysis |
| Title | Motion Artifact Removal in Functional Near‐Infrared Spectroscopy Based on Long Short‐Term Memory‐Autoencoder Model |
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