Seizures

EEG-ReMinD: Enhancing Neurodegenerative EEG Decoding through Self-Supervised State Reconstruction-Primed Riemannian Dynamics

The development of EEG decoding algorithms confronts challenges such as data sparsity, subject variability, and the need for precise annotations, all of which are vital for advancing brain-computer interfaces and enhancing the diagnosis of diseases. To address these issues, we propose a novel two-stage approach named Self-Supervised State Reconstruction-Primed Riemannian Dynamics (EEG-ReMinD) , ...

Exploring the distribution of connectivity weights in resting-state EEG networks

The resting-state brain networks (RSNs) reflects the functional connectivity patterns between brain modules, providing essential foundations for decoding intrinsic neural information within the brain. It serves as one of the primary tools for describing the spatial dynamics of the brain using various neuroimaging techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG). ...

Perception-Guided EEG Analysis: A Deep Learning Approach Inspired by Level of Detail (LOD) Theory

Objective: This study explores a novel deep learning approach for EEG analysis and perceptual state guidance, inspired by Level of Detail (LOD) theo...

Motif Discovery Framework for Psychiatric EEG Data Classification

In current medical practice, patients undergoing depression treatment must wait four to six weeks before a clinician can assess medication response ...

Exploring EEG and Eye Movement Fusion for Multi-Class Target RSVP-BCI

Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interfaces (BCIs) facilitate high-throughput target image detection by identifying even...

Integrating Language-Image Prior into EEG Decoding for Cross-Task Zero-Calibration RSVP-BCI

Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an effective technology used for information detection by detecting ...

Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior

A Magnetoencephalography (MEG) time-series recording consists of multi-channel signals collected by superconducting sensors, with each signal's inte...

Brainwide hemodynamics predict EEG neural rhythms across sleep and wakefulness in humans

The brain exhibits rich oscillatory dynamics that play critical roles in vigilance and cognition, such as the neural rhythms that define sleep. These ...

Linking age changes in human cortical microcircuits to impaired brain function and EEG biomarkers

Human brain aging involves a variety of cellular and synaptic changes, but how these changes affect brain function and signals remains poorly understo...

Recognizing EEG responses to active TMS vs. sham stimulations in different TMS-EEG datasets: a machine learning approach

Transcranial Magnetic Stimulation (TMS) with simultaneous Electroencephalogram (TMS-EEG) allows assessing the neurophysiological properties of cortica...

A Wearable Brain–Computer Interface for Mitigating Car Sickness via Attention Shifting

Car sickness, an enormous vehicular travel challenge, affects a significant proportion of the population. Pharmacological interventions are limited by...

Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images

Remarkably, human brains have the ability to accurately perceive and process the real-world size of objects, despite vast differences in distance and ...

Classifier-guided Deep Oscillatory Neural Networks (cDONN) for capturing both Neural Dynamics and Behavior simultaneously

Generating EEG signals alongside behavioural actions introduces substantial biological complexity, akin to an abstract model that mimics rich oscillat...

Cognitive training effects are shaped more by individual brain dynamics than age – Evidence from younger and older women

Given the well-established structural and functional changes in the aging brain, it is widely assumed that cognitive aging is primarily driven by robu...

Simultaneous cortical tracking of competing speech streams during attention switching

Successful speech communication in multi-talker scenarios requires a skilful combination of sustained attention and rapid attention switching. While t...

Uncertainty in Deep Learning for EEG under Dataset Shifts

As artificial intelligence (AI) is increasingly integrated into medical diagnostics, it is essential that predictive models provide not only accurate ...

Machine Learning Resolves Functional Phenotypes and Therapeutic Responses in KCNQ2 Developmental Epileptic Encephalopathy iPSC Models

Pathogenic KCNQ2 variants are associated with developmental and epileptic encephalopathy (KCNQ2-DEE), a devastating disorder characterized by neonatal...

Predicting Drug Response with Multi-Task Gradient-Boosted Trees in Epilepsy

Despite the availability of numerous anti-seizure medications (ASMs), drug resistance remains a major issue for people with epilepsy. The probability ...

Functional Connectivity in Self-limited Epilepsy with Centrotemporal Spikes (SeLECTS) Increases with Epilepsy Duration and Interictal Spike Exposure

To determine the impact of epilepsy duration and interictal spikes on functional connectivity in children with Self-Limited Epilepsy with Centrotempor...

Where is the melody? Spontaneous attention orchestrates melody formation during polyphonic music listening

Humans seamlessly process multi-voice music into a coherent perceptual whole. Yet the neural strategies supporting this experience remain unclear. One...