Medical Xpress

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

Electroencephalography (EEG) is a fascinating noninvasive technique that measures and records the brain's electrical activity. It detects small electrical signals produced when neurons in the brain ...
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 ...
GitHub

EEG Motor Imagery Classification

The goal of this project is to classify EEG signals recorded during motor imagery tasks—that is, when a subject imagines moving a limb (e.g., left or right hand). By decoding these imagined movements ...
IEEE

Motor Imagery EEG Signal Classification Method Based on SPD Manifold and Gaussian SPD Manifold Kernel Features

Abstract: Motor imagery EEG (MI-EEG) signal classification is one of the key challenges in Brain-computer interface (BCI) technology. Currently, MI-EEG signal analysis methods based on Riemannian ...
Frontiers

Transfer Learning for Subject-Independent Motor Imagery EEG Classification Using Convolutional Relational Networks

The final, formatted version of the article will be published soon. Motor imagery (MI) based electroencephalography (EEG) classification is central to brain–computer interface (BCI) research but ...
eLife

Visuomotor mismatch EEG responses in occipital cortex of freely moving human subjects

This valuable study demonstrates that self-motion strongly affects neural responses to visual stimuli, comparing humans moving through a virtual environment to passive viewing. However, evidence that ...
IEEE

Enhancing Neural Representations of Motor Imagery Through Action-Specific Brain Connectivity Patterns

Abstract: Motor imagery (MI) is a cognitive process that allows individuals to mentally simulate movements without physical executio n. However, the exploration of functional connectivity (FC) and ...
Frontiers

Hilbert-Huang Transform Embedded Self-Attention Neural Network for EEG-based major depressive disorder vs. healthy controls classification

This paper proposes a novel approach for distinguishing Major Depressive Disorder (MDD) patients from healthy controls (HC), namely depression screening, using EEG signals, where the Hilbert-Huang ...