M
Martin Luessi
Researcher at Harvard University
Publications - 25
Citations - 4384
Martin Luessi is an academic researcher from Harvard University. The author has contributed to research in topics: Bayesian inference & Rank (linear algebra). The author has an hindex of 14, co-authored 25 publications receiving 3182 citations. Previous affiliations of Martin Luessi include Northwestern University & Massachusetts Institute of Technology.
Papers
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Journal ArticleDOI
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.
Journal ArticleDOI
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.
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 is an open-source software package that addresses this challenge by providing 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.
Journal ArticleDOI
Sparse Bayesian Methods for Low-Rank Matrix Estimation
TL;DR: In this paper, a matrix factorization formulation and enforcing the low-rank constraint in the estimates as a sparsity constraint are used to determine the correct rank while providing high recovery performance.
Journal ArticleDOI
Compressive Light Field Sensing
TL;DR: The proposed acquisition and recovery method provides light field images with high spatial resolution and signal-to-noise-ratio, and therefore is not affected by limitations common to existing light field camera designs.