165 citations · 213 across the 6 of their papers we have counts for
6 papers
Noise Morphing for Audio Time Stretching
Eloi Moliner, Leonardo Fierro, Alec Wright +2
This letter introduces an innovative method to enhance the quality of audio time stretching by precisely decomposing a sound into sines, transients, and noise and by improving the…
Online functional connectivity analysis of large all-to-all networks
Lorenz Esch, Jinlong Dong, Matti Hämäläinen +3
The analysis of EEG/MEG functional connectivity has become an important tool in neural research. Especially the high time resolution of EEG/MEG enables important insight into the f…
Contextual Minimum-Norm Estimates (CMNE): A Deep Learning Method for Source Estimation in Neuronal Networks
Christoph Dinh, John GW Samuelsson, Alexander Hunold +2
Magnetoencephalography (MEG) and Electroencephalography (EEG) source estimates have thus far mostly been derived sample by sample, i.e., independent of each other in time. However,…
Sparsity Enables Estimation of both Subcortical and Cortical Activity from MEG and EEG
Pavitra Krishnaswamy, Gabriel Obregon-Henao, Jyrki Ahveninen +5
Subcortical structures play a critical role in brain function. However, options for assessing electrophysiological activity in these structures are limited. Electromagnetic fields…
An Analysis of How Spatiotemporal Dynamic Models of Brain Activity Could Improve MEG/EEG Inverse Solutions
Camilo Lamus, Matti S. Hamalainen, Emery N. Brown +1
MEG and EEG are noninvasive functional neuroimaging techniques that provide recordings of brain activity with high temporal resolution, and thus provide a unique window to study fa…
State-space solutions to the dynamic magnetoencephalography inverse problem using high performance computing
Christopher J. Long, Patrick L. Purdon, Simona Temereanca +3
Determining the magnitude and location of neural sources within the brain that are responsible for generating magnetoencephalography (MEG) signals measured on the surface of the he…