Sensor-level MEG combined with machine learning yields robust classification of mild traumatic brain injury patients.

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Bibliographic Details
Title: Sensor-level MEG combined with machine learning yields robust classification of mild traumatic brain injury patients.
Authors: Aaltonen J; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, P.O. Box 340, 00029 HUS Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto University, P.O. Box 12200, 00760 AALTO, Finland. Electronic address: juho.aaltonen@aalto.fi., Heikkinen V; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, P.O. Box 340, 00029 HUS Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto University, P.O. Box 12200, 00760 AALTO, Finland., Kaltiainen H; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto University, P.O. Box 12200, 00760 AALTO, Finland; Department of Neurology, Helsinki University Hospital and Clinical Neurosciences, Neurology, University of Helsinki, P.O. Box 340, 00029 HUS, Helsinki, Finland., Salmelin R; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto University, P.O. Box 12200, 00760 AALTO, Finland., Renvall H; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, P.O. Box 340, 00029 HUS Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto University, P.O. Box 12200, 00760 AALTO, Finland; Department of Neurology, Helsinki University Hospital and Clinical Neurosciences, Neurology, University of Helsinki, P.O. Box 340, 00029 HUS, Helsinki, Finland.
Source: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology [Clin Neurophysiol] 2023 Sep; Vol. 153, pp. 79-87. Date of Electronic Publication: 2023 Jun 30.
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal Info: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 100883319 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-8952 (Electronic) Linking ISSN: 13882457 NLM ISO Abbreviation: Clin Neurophysiol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1872-8952
DOI:10.1016/j.clinph.2023.06.010