no code implementations • 16 Dec 2023 • Nhan D. T. Nguyen, Kaare Mikkelsen, Preben Kidmose
This study proposes a novel approach to auditory attention decoding by looking at higher-level cognitive responses to natural speech.
1 code implementation • 9 Jan 2023 • Huy Phan, Kristian P. Lorenzen, Elisabeth Heremans, Oliver Y. Chén, Minh C. Tran, Philipp Koch, Alfred Mertins, Mathias Baumert, Kaare Mikkelsen, Maarten De Vos
In this work, we show that while encoding the logic of a whole sleep cycle is crucial to improve sleep staging performance, the sequential modelling approach in existing state-of-the-art deep learning models are inefficient for that purpose.
no code implementations • 3 Nov 2021 • Huy Phan, Kaare Mikkelsen
Modern deep learning holds a great potential to transform clinical practice on human sleep.
no code implementations • 23 May 2021 • Huy Phan, Kaare Mikkelsen, Oliver Y. Chén, Philipp Koch, Alfred Mertins, Maarten De Vos
It is based on the transformer backbone and offers interpretability of the model's decisions at both the epoch and sequence level.
no code implementations • 23 Apr 2020 • Huy Phan, Kaare Mikkelsen, Oliver Y. Chén, Philipp Koch, Alfred Mertins, Preben Kidmose, Maarten De Vos
We employ the pretrained SeqSleepNet (i. e. the subject independent model) as a starting point and finetune it with the single-night personalization data to derive the personalized model.
no code implementations • 8 Jan 2018 • Kaare Mikkelsen, Maarten De Vos
Starting from a general convolutional neural network architecture, we allow the model to learn individual characteristics of the first night of sleep in order to quantify sleep stages of the second night.
Neurons and Cognition