activity
20172019
most citedDeep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

physics.med-ph2019

Accessibility of Cortical Regions to Focal TES: Dependence on Spatial Position, Safety, and Practical Constraints

Guilherme Bicalho Saturnino, Hartwig Roman Siebner, Axel Thielscher +1

Transcranial electric stimulation (TES) can modulate intrinsic neural activity in the brain by injecting weak currents through electrodes attached to the scalp. TES has been widely…

econ.GN2019

Ergodicity-breaking reveals time optimal decision making in humans

David Meder, Finn Rabe, Tobias Morville +5

Ergodicity describes an equivalence between the expectation value and the time average of observables. Applied to human behaviour, ergodic theories of decision-making reveal how in…

stat.ML2018

Probabilistic PARAFAC2

Philip J. H. Jørgensen, Søren F. V. Nielsen, Jesper L. Hinrich +3

The PARAFAC2 is a multimodal factor analysis model suitable for analyzing multi-way data when one of the modes has incomparable observation units, for example because of difference…

cs.CV20171 cited

Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring

Albert Vilamala, Kristoffer H. Madsen, Lars K. Hansen

Sleep studies are important for diagnosing sleep disorders such as insomnia, narcolepsy or sleep apnea. They rely on manual scoring of sleep stages from raw polisomnography signals…

cs.CV2017

Adaptive Smoothing in fMRI Data Processing Neural Networks

Albert Vilamala, Kristoffer Hougaard Madsen, Lars Kai Hansen

Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data processing pipelines to accurately determine brain activity; among them, the crucial step of spatial smoothin…