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Deep learning-based motion artifact removal in functional near-infrared spectroscopy.
Gao, Yuanyuan; Chao, Hanqing; Cavuoto, Lora; Yan, Pingkun; Kruger, Uwe; Norfleet, Jack E; Makled, Basiel A; Schwaitzberg, Steven; De, Suvranu; Intes, Xavier.
Affiliation
  • Gao Y; Rensselaer Polytechnic Institute, Center for Modeling, Simulation and Imaging in Medicine, Troy, New York, United States.
  • Chao H; Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States.
  • Cavuoto L; University at Buffalo, Department of Industrial and Systems Engineering, Buffalo, New York, United States.
  • Yan P; Rensselaer Polytechnic Institute, Center for Modeling, Simulation and Imaging in Medicine, Troy, New York, United States.
  • Kruger U; Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States.
  • Norfleet JE; Rensselaer Polytechnic Institute, Center for Modeling, Simulation and Imaging in Medicine, Troy, New York, United States.
  • Makled BA; Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States.
  • Schwaitzberg S; U.S. Army Combat Capabilities Development Command-Soldier Center, Orlando, Florida, United States.
  • De S; SFC Paul Ray Smith Simulation and Training Technology Center, Orlando, Florida, United States.
  • Intes X; Medical Simulation Research Branch, Orlando, Florida, United States.
Neurophotonics ; 9(4): 041406, 2022 Oct.
Article in En | MEDLINE | ID: mdl-35475257
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Neurophotonics Year: 2022 Document type: Article Affiliation country: Estados Unidos Country of publication: Estados Unidos
Fulltext
Add to My VHL
Print
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PubMed Links
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Neurophotonics Year: 2022 Document type: Article Affiliation country: Estados Unidos Country of publication: Estados Unidos