Deep learning turns weather satellite thermal imagery into hourly ocean current maps

Scientists have developed a new method to measure ocean surface currents over large areas in greater detail than ever before. Called GOFLOW (Geostationary Ocean Flow), the approach applies deep ...
Morning Overview on MSN

ChatGPT can speed learning but may weaken long-term retention

A growing body of experimental research suggests that using ChatGPT can boost short-term performance on some tasks, but may ...
Frontiers

Editorial: Deep learning for high-dimensional sense, non-linear signal processing and intelligent diagnosis, vol II

Psychiatry stands at a pivotal turning point shaped by rapid technological advances and pressing clinical demands (1). Mental health disorders, defined by multifaceted etiologies and heterogeneous ...
Fox News

IG: Hegseth broke Pentagon rules using Signal to share strike details, though no classified info was released

A Pentagon inspector general report concluded that Secretary of War Pete Hegseth sent sensitive, nonpublic strike information over the encrypted app Signal using his personal phone, a violation of ...
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 ...
IEEE

A Novel Deep Learning Framework for High-Gamma EEG Signal Classification Using Hybrid Compact-CNN-LSTM Network

Abstract: Electroencephalography (EEG)-based motor imagery (MI) classification plays a crucial role in developing efficient brain-computer interface (BCI) systems. However, decoding EEG signals is ...
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.
EurekAlert!

Attention detection using EEG signals and machine learning: A review

An overview of attention detection using EEG signals, which includes six steps: an experimental paradigm design, in which the task and the stimuli are defined and presented to the subjects; EEG data ...