5 papers · 1 filter
Decoding Imagined Speech: A Strictly Subject-Independent Approach Using EEG
Frederik Møllskov Trier, Xiaopeng Mao, Sadasivan Puthusserypady
Imagined speech decoding from electroencephalography (EEG) has gained increasing attention as a potential communication pathway for individuals with severe motor impairments, yet r…
Wearable ECG Quality Assessment: A Deep Learning and Ambulatory Context-Awareness Approach
Xiaopeng Mao, Marike Weisbjerg, Sadasivan Puthusserypady
This paper presents and evaluates a Deep Learning-based (DL-based) Signal Quality Assessment (SQA) model to distinguish between clean and noisy ambulatory Electrocardiograms (ECG).…
FOUND-AF: Benchmarking ECG Foundation Models for Atrial Fibrillation Detection
Amirhossein Taleshinosrati, Yangyang Wang, Atitaya Phoemsuk +5
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with increased risks of stroke, heart failure, and mortality. Recent ECG foundation model…
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…
RAG-based EEG-to-Text Translation Using Deep Learning and LLMs
Enrico Collautti, Xiaopeng Mao, Luca Tonin +2
The decoding of linguistic information from electroencephalography (EEG) signals remains an extremely challenging problem in brain-computer interface (BCI) research. In particular,…