BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

151 related articles for article (PubMed ID: 38033535)

  • 1. Learning based motion artifacts processing in fNIRS: a mini review.
    Zhao Y; Luo H; Chen J; Loureiro R; Yang S; Zhao H
    Front Neurosci; 2023; 17():1280590. PubMed ID: 38033535
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Deep learning-based motion artifact removal in functional near-infrared spectroscopy.
    Gao Y; Chao H; Cavuoto L; Yan P; Kruger U; Norfleet JE; Makled BA; Schwaitzberg S; De S; Intes X
    Neurophotonics; 2022 Oct; 9(4):041406. PubMed ID: 35475257
    [No Abstract]   [Full Text] [Related]  

  • 3. Motion artifacts in functional near-infrared spectroscopy: a comparison of motion correction techniques applied to real cognitive data.
    Brigadoi S; Ceccherini L; Cutini S; Scarpa F; Scatturin P; Selb J; Gagnon L; Boas DA; Cooper RJ
    Neuroimage; 2014 Jan; 85 Pt 1(0 1):181-91. PubMed ID: 23639260
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Comparison of motion correction techniques applied to functional near-infrared spectroscopy data from children.
    Hu XS; Arredondo MM; Gomba M; Confer N; DaSilva AF; Johnson TD; Shalinsky M; Kovelman I
    J Biomed Opt; 2015; 20(12):126003. PubMed ID: 26662300
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A Motion Artifact Correction Procedure for fNIRS Signals Based on Wavelet Transform and Infrared Thermography Video Tracking.
    Perpetuini D; Cardone D; Filippini C; Chiarelli AM; Merla A
    Sensors (Basel); 2021 Jul; 21(15):. PubMed ID: 34372353
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy.
    von Lühmann A; Boukouvalas Z; Müller KR; Adalı T
    Neuroimage; 2019 Oct; 200():72-88. PubMed ID: 31203024
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Wavelet-based motion artifact removal for functional near-infrared spectroscopy.
    Molavi B; Dumont GA
    Physiol Meas; 2012 Feb; 33(2):259-70. PubMed ID: 22273765
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Recommendations for motion correction of infant fNIRS data applicable to multiple data sets and acquisition systems.
    Di Lorenzo R; Pirazzoli L; Blasi A; Bulgarelli C; Hakuno Y; Minagawa Y; Brigadoi S
    Neuroimage; 2019 Oct; 200():511-527. PubMed ID: 31247300
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis.
    Hossain MS; Chowdhury MEH; Reaz MBI; Ali SHM; Bakar AAA; Kiranyaz S; Khandakar A; Alhatou M; Habib R; Hossain MM
    Sensors (Basel); 2022 Apr; 22(9):. PubMed ID: 35590859
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A deep convolutional neural network for estimating hemodynamic response function with reduction of motion artifacts in fNIRS.
    Kim M; Lee S; Dan I; Tak S
    J Neural Eng; 2022 Feb; 19(1):. PubMed ID: 35038682
    [No Abstract]   [Full Text] [Related]  

  • 11. A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data.
    Chiarelli AM; Maclin EL; Fabiani M; Gratton G
    Neuroimage; 2015 May; 112():128-137. PubMed ID: 25747916
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Improved Motion Artifact Correction in fNIRS Data by Combining Wavelet and Correlation-Based Signal Improvement.
    Al-Omairi HR; Fudickar S; Hein A; Rieger JW
    Sensors (Basel); 2023 Apr; 23(8):. PubMed ID: 37112320
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Motion artifacts removal and evaluation techniques for functional near-infrared spectroscopy signals: A review.
    Huang R; Hong KS; Yang D; Huang G
    Front Neurosci; 2022; 16():878750. PubMed ID: 36263362
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Automatic brain MRI motion artifact detection based on end-to-end deep learning is similarly effective as traditional machine learning trained on image quality metrics.
    Vakli P; Weiss B; Szalma J; Barsi P; Gyuricza I; Kemenczky P; Somogyi E; Nárai Á; Gál V; Hermann P; Vidnyánszky Z
    Med Image Anal; 2023 Aug; 88():102850. PubMed ID: 37263108
    [TBL] [Abstract][Full Text] [Related]  

  • 15. An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection.
    Ercan R; Xia Y; Zhao Y; Loureiro R; Yang S; Zhao H
    IEEE Trans Very Large Scale Integr VLSI Syst; 2024 Apr; 32(4):763-773. PubMed ID: 38765316
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Benchmarking framework for machine learning classification from fNIRS data.
    Benerradi J; Clos J; Landowska A; Valstar MF; Wilson ML
    Front Neuroergon; 2023; 4():994969. PubMed ID: 38234474
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Hybrid motion artifact detection and correction approach for functional near-infrared spectroscopy measurements.
    Gao L; Wei Y; Wang Y; Wang G; Zhang Q; Zhang J; Chen X; Yan X
    J Biomed Opt; 2022 Feb; 27(2):. PubMed ID: 35212200
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Motion Artifact Correction of Multi-Measured Functional Near-Infrared Spectroscopy Signals Based on Signal Reconstruction Using an Artificial Neural Network.
    Lee G; Jin SH; An J
    Sensors (Basel); 2018 Sep; 18(9):. PubMed ID: 30189651
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Controlling jaw-related motion artifacts in functional near-infrared spectroscopy.
    Zhang F; Reid A; Schroeder A; Ding L; Yuan H
    J Neurosci Methods; 2023 Mar; 388():109810. PubMed ID: 36738847
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Optimizing spatial specificity and signal quality in fNIRS: an overview of potential challenges and possible options for improving the reliability of real-time applications.
    Klein F
    Front Neuroergon; 2024; 5():1286586. PubMed ID: 38903906
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.