5 citations · 7 across the 7 of their papers we have counts for
7 papers
Learning transferable human physiology from two million hours of sleep with SleepFM-2
Rahul Thapa, Christopher Sun, William Theodor Lehn-Schioler +14
Sleep provides a nightly window into health by capturing coordinated activity across the brain, heart, muscles and respiratory system. We introduce SleepFM-2, a sleep foundation mo…
Real-Time EEG Cap Electrode Detection for Guided Point-of-Care Placement
William Lehn-Schiøler, Mads Sverker Nilsson, Nicki Skafte Detlefsen
We present a two-stage vision system that detects EEG cap electrodes in a live webcam stream and validates their anatomical placement in real time. A single-class YOLO detector loc…
Clinical Feasibility of Smartphone-based EEG in Kenya
William Lehn-Schiøler, Nomin Enkhtsetseg, Anton Mosquera Storgaard +8
Purpose: Access to electroencephalography (EEG) remains limited across low- and middle-income countries (LMICs) due to cost, infrastructure requirements, and a shortage of trained…
Mechanistic Interpretability of EEG Foundation Models via Sparse Autoencoders
William Lehn-Schiøler, Magnus Ruud Kjær, Rahul Thapa +10
EEG foundation models achieve state-of-the-art clinical performance, yet the internal computations driving their predictions remain opaque: a barrier to clinical trust. We apply To…
Pretraining on Sleep Data Improves non-Sleep Biosignal Tasks
William Lehn-Schiøler, Magnus Ruud Kjær, Phillip Hempel +7
Sleep foundation models have recently demonstrated strong performance on in-domain polysomnography tasks, including sleep staging, apnea detection, and disease risk prediction. In…
SPEED: Scalable Preprocessing of EEG Data for Self-Supervised Learning
Anders Gjølbye, Lina Skerath, William Lehn-Schiøler +2
Electroencephalography (EEG) research typically focuses on tasks with narrowly defined objectives, but recent studies are expanding into the use of unlabeled data within larger mod…
Concept-based explainability for an EEG transformer model
Anders Gjølbye, William Lehn-Schiøler, Áshildur Jónsdóttir +2
Deep learning models are complex due to their size, structure, and inherent randomness in training procedures. Additional complexity arises from the selection of datasets and induc…