348 citations · 591 across the 11 of their papers we have counts for
12 papers
Unsupervised domain transfer: Overcoming signal degradation in sleep monitoring by increasing scoring realism
Mohammad Ahangarkiasari, Andreas Tind Damgaard, Casper Haurum +1
Objective: Investigate whether hypnogram 'realism' can be used to guide an unsupervised method for handling arbitrary types of signal degradation in mobile sleep monitoring. Approa…
Fully-automated sleep staging: multicenter validation of a generalizable deep neural network for Parkinson's disease and isolated REM sleep behavior disorder
Jesper Strøm, Casper Skjærbæk, Natasha Becker Bertelsen +11
Isolated REM sleep behavior disorder (iRBD) is a key prodromal marker of Parkinson's disease (PD), and video-polysomnography (vPSG) remains the diagnostic gold standard. However, m…
Single-word Auditory Attention Decoding Using Deep Learning Model
Nhan Duc Thanh Nguyen, Huy Phan, Kaare Mikkelsen +1
Identifying auditory attention by comparing auditory stimuli and corresponding brain responses, is known as auditory attention decoding (AAD). The majority of AAD algorithms utiliz…
AADNet: An End-to-End Deep Learning Model for Auditory Attention Decoding
Nhan Duc Thanh Nguyen, Huy Phan, Simon Geirnaert +2
Auditory attention decoding (AAD) is the process of identifying the attended speech in a multi-talker environment using brain signals, typically recorded through electroencephalogr…
Study of cognitive component of auditory attention to natural speech events
Nhan D. T. Nguyen, Kaare Mikkelsen, Preben Kidmose
Event-related potentials (ERP) have been used to address a wide range of research questions in neuroscience and cognitive psychology including selective auditory attention. The rec…
L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging
Huy Phan, Kristian P. Lorenzen, Elisabeth Heremans +7
Human sleep is cyclical with a period of approximately 90 minutes, implying long temporal dependency in the sleep data. Yet, exploring this long-term dependency when developing sle…
Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions
Huy Phan, Kaare Mikkelsen
Modern deep learning holds a great potential to transform clinical practice on human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workloa…
SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification
Huy Phan, Kaare Mikkelsen, Oliver Y. Chén +3
Background: Black-box skepticism is one of the main hindrances impeding deep-learning-based automatic sleep scoring from being used in clinical environments. Methods: Towards inter…
Light-weight sleep monitoring: electrode distance matters more than placement for automatic scoring
Kaare B. Mikkelsen, Huy Phan, Mike L. Rank +3
Modern sleep monitoring development is shifting towards the use of unobtrusive sensors combined with algorithms for automatic sleep scoring. Many different combinations of wet and…
Personalized Automatic Sleep Staging with Single-Night Data: a Pilot Study with KL-Divergence Regularization
Huy Phan, Kaare Mikkelsen, Oliver Y. Chén +4
Brain waves vary between people. An obvious way to improve automatic sleep staging for longitudinal sleep monitoring is personalization of algorithms based on individual characteri…
Personalizing deep learning models for automatic sleep staging
Kaare Mikkelsen, Maarten de Vos
Despite continued advancement in machine learning algorithms and increasing availability of large data sets, there is still no universally acceptable solution for automatic sleep s…
Threshold games and cooperation on multiplayer graphs
Kaare B. Mikkelsen, Lars A. Bach
Objective: The study investigates the effect on cooperation in multiplayer games, when the population from which all individuals are drawn is structured - i.e. when a given individ…