AI powered analysis of routine EEG scans is now distinguishing Alzheimer’s disease from frontotemporal dementia while also estimating disease severity, offering faster and more affordable pathways to ...
Dr Andrei Alexandrov discusses his experience implementing point-of-care EEG equipped with artificial intelligence. As neurologists, our responsibility goes beyond interpreting electroencephalograms ...
In this study, researchers developed a deep learning framework to analyse EEG signals from individuals with Alzheimer’s disease, frontotemporal dementia, and cognitively normal controls. The model ...
Summary: New research shows that deep learning can use EEG signals to distinguish Alzheimer’s disease from frontotemporal dementia with high accuracy. By analyzing both the timing and frequency of ...
Explore the first part of our series on sleep stage classification using Python, EEG data, and powerful libraries like Sklearn and MNE. Perfect for data scientists and neuroscience enthusiasts!
Start your journey into machine learning with EEG time-series data in this easy-to-follow Python project. Perfect for beginners looking to explore brain signal analysis! #MachineLearning #EEG ...
Introduction: Brain-computer interfaces (BCIs) leverage EEG signal processing to enable human-machine communication and have broad application potential. However, existing deep learning-based BCI ...
Abstract: Modern society faces significant challenges related to stress, sadness, and panic. Notably, stress is a major predictor of health disparities linked to socioeconomics. To predict and ...
Abstract: Electroencephalography (EEG)-based seizure prediction has garnered significant attention in epilepsy management, with deep learning methods enhancing prediction performance. However, ...
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