Abstract: Depression is a debilitating and enervating mental health disorder that requires attention for necessitating accurate and efficient diagnostic techniques ...
New AI model decodes brain signals captured noninvasively via EEG opens the possibility of developing future neuroprosthetics ...
A machine learning model trained on EEG data from patients recovering from strokes helps predict how new patients will regain ...
An individual may become completely paralyzed because of any number of accidents that interfere with the functioning of the ...
Researchers at örebro University have developed two new AI models that can analyze the brain's electrical activity and accurately distinguish between healthy individuals and patients with dementia, ...
Researchers develop a novel topology-aware multiscale feature fusion network to enhance the accuracy and robustness of EEG-based motor imagery decoding Electroencephalography (EEG) is a fascinating ...
Researchers have discovered there was an anomaly in Earth's gravitational field between 2006 and 2008, potentially caused by a mineral shift deep within Earth's mantle. GRACE satellites detected a ...
This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...
Abstract: Alzheimer's disease (AD), is a prevalent neurodegenerative disorder, characterized by cognitive decline. Alongside AD, and Frontotemporal dementia (FTD) poses significant challenges in ...
Electroencephalogram (EEG) signal analysis plays a vital role in diagnosing and monitoring alcoholism, where accurate classification of individuals into alcoholic and control groups is essential.
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