M
Matti Hämäläinen
Researcher at Harvard University
Publications - 535
Citations - 37125
Matti Hämäläinen is an academic researcher from Harvard University. The author has contributed to research in topics: Magnetoencephalography & Ultra-wideband. The author has an hindex of 78, co-authored 514 publications receiving 33188 citations. Previous affiliations of Matti Hämäläinen include Massachusetts Institute of Technology & Yokohama National University.
Papers
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Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain
TL;DR: The mathematical theory of the method is explained in detail, followed by a thorough description of MEG instrumentation, data analysis, and practical construction of multi-SQUID devices.
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Interpreting magnetic fields of the brain: minimum norm estimates
TL;DR: In this procedure, essentially nothing is assumed about the source currents, except that they are spatially restricted to a certain region, and the results can describe the structure of the current flow fairly well.
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MEG and EEG data analysis with MNE-Python
Alexandre Gramfort,Martin Luessi,Eric B. Larson,Denis A. Engemann,Daniel Strohmeier,Christian Brodbeck,Roman Goj,Mainak Jas,Teon L Brooks,Lauri Parkkonen,Matti Hämäläinen +10 more
TL;DR: MNE-Python as discussed by the authors is an open-source software package that provides state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions.
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Dynamic imaging of coherent sources: Studying neural interactions in the human brain
TL;DR: Methods for estimating functional connectivities between brain areas will facilitate characterization of cortical networks involved in sensory, motor, or cognitive tasks and will allow investigation of pathological connectivities in neurological disorders.
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MNE software for processing MEG and EEG data
Alexandre Gramfort,Martin Luessi,Eric B. Larson,Denis A. Engemann,Daniel Strohmeier,Christian Brodbeck,Lauri Parkkonen,Matti Hämäläinen +7 more
TL;DR: Detailed information about the MNE package is given and typical use cases are described while also warning about potential caveats in analysis.