Noninvasive brain scanning could restore movement after spinal cord injury

An individual may become completely paralyzed because of any number of accidents that interfere with the functioning of the ...
The New York Times

Pentagon Investigator Faults Hegseth for Improper Use of Signal

An inspector general report to be released on Thursday examined the defense secretary’s use of a private messaging app to discuss airstrikes in Yemen. By Robert Jimison Megan Mineiro and John Ismay ...
News Medical

New AI models detect dementia with high accuracy using EEG signals

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, ...
EurekAlert!

Topology-aware deep learning model enhances EEG-based motor imagery decoding

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 ...
Frontiers

Deep learning for inner speech recognition: a pilot comparative study of EEGNet and a spectro-temporal Transformer on bimodal EEG-fMRI data

1 Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia 2 Center of Excellence in Intelligent Engineering Systems (CEIES), King ...
IEEE

Advancing EEG Analysis: A Novel Graph Signal Processing Approach for Optimal Feature Extraction in Parkinson’s Disease Detection

Abstract: Graph Signal Processing (GSP), an emerging field, provides a flexible framework to model and analyze Electroencephalogram (EEG) sensor data that exhibit intricate relationships and ...
GitHub

Self-Learning-VAE-for-EEG-Artifact-Removal

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 ...
IEEE

NeuroFormer: A Deep Learning Framework for Alzheimer's Detection Using EEG Signals

Abstract: Alzheimer's disease (AD), is a prevalent neurodegenerative disorder, characterized by cognitive decline. Alongside AD, and Frontotemporal dementia (FTD) poses significant challenges in ...
Frontiers

Improving EEG classification of alcoholic and control subjects using DWT-CNN-BiGRU with various noise filtering techniques

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.