Seizures

STAND-Net: A Spiking Temporal Attention autoeNcoDer Network for Efficient EEG Artifact Removal.

Electroencephalography (EEG)-based brain computer interface (BCI) systems hold significant promise across diverse applications; however, their performance is compromised by pervasive physiological artifacts that degrade signal fidelity. While current deep neural networks (DNNs) improve artifact rejection, their high computational cost precludes deployment in wearable BCIs systems. Here, we introdu...

Adaptive Segmentation of EEG for Machine Learning Applications.

OBJECTIVE: Electroencephalography (EEG) data is derived by sampling continuous neurological time series signals. In order to prepare EEG signals for machine learning, the signal must be divided into manageable segments. The current naive approach uses arbitrary fixed time slices, which may have limited biological relevance because brain states are not confined to fixed intervals. We investigate wh...

Interpretable EEG biomarkers for neurological disease models in mice using bag-of-waves classifiers.

OBJECTIVE: Electroencephalograms (EEGs) are time-series records of the electrical potential from collective neural activity in the brain. EEG waveform...

A CNN-transformer fusion for EEG-based discrimination of Alzheimer's and frontotemporal dementia.

Dementia is a progressive neurodegenerative disorder that severely impacts cognitive functions and daily living, especially in aging populations. Amon...

WGB-GLFI: A Novel Graph-Based Global-Local Feature Interaction Framework for Automated Seizure Detection.

Epilepsy detection faces significant challenges due to unpredictable seizures, ranging from brief awareness lapses to severe convulsions, posing risks...

Multimodal Wearable Sensor-Based Stress Detection: Machine Learning Pipeline with Systematic Feature Selection and Key Biomarker Insights.

The increasing awareness of stress-related health impacts has driven demand for accurate, non-invasive stress detection methods, particularly those le...

Cannabidiol inhibits both human KV7.1 and KV7.1/KCNE1 channels through distinct sites.

Several essential physiological systems express voltage-gated potassium channels within the KV7 family (comprising KV7.1-7.5), sometimes also co-assem...

A novel framework for cognitive state identification using resting-state EEG.

Recent advancements in cognitive impairment research have led to significant progress. Electroencephalography (EEG)-based cognitive state identificati...

The research progress of wearable digital health technologies in epilepsy management.

Epilepsy is one of the most common neurological disorders, characterized by recurrent, unpredictable seizures. Due to the unpredictability of seizures...

Automatic EEG artifact detection using a local-global feature fusion network in time and time-frequency domains.

BACKGROUND: Video electroencephalographies (VEEGs) are often affected by artifacts, which can diminish clinicians' efficiency in interpreting VEEG dat...

Predicting seizure-free outcomes in people with treatment-resistant epilepsy: A machine learning approach.

BACKGROUND: Epilepsy surgery is an important intervention for treatment-resistant epilepsy, butthe ability to predict long-term seizure freedom post-s...

Automatic sleep scoring for real-time monitoring and stimulation in individuals with and without sleep apnea.

Digital therapeutics, enabled by advanced machine learning algorithms and medical wearable devices, offer a promising approach to streamline diagnosti...

DoC-Informer: Automated Discrimination of Disorders of Consciousness under Adaptive EEG Settings.

OBJECTIVE: Disorders of consciousness (DoC) diagnosis critically depends on accurate state discrimination to guide treatment and prognosis. Current EE...

Neuroimaging-Based Deep Learning Applications for Lesion Detection and Predicting the Outcome Following Epilepsy Surgery.

Neuroimaging studies are essential for evaluating patients with drug-resistant focal epilepsy and determining their candidacy for epilepsy surgery. Th...

Task-evoked brain network dynamics underlying cognitive control using source-localized EEG and machine learning.

OBJECTIVE: Functional connectivity (FC) coordinates brain activity during cognitive tasks, yet the influence of demographic variables and health facto...

EEG microstate-derived dynamic network biomarkers for lateralization and structural etiology in temporal lobe epilepsy.

OBJECTIVE: Temporal lobe epilepsy (TLE) is the most common focal epilepsy but remains highly heterogeneous across hemispheric and structural etiology....

Development and validation of interpretable multimodal clinical-radiomics models for predicting epileptogenic foci and surgical outcomes in tuberous sclerosis complex: A multicenter study.

Precise localization and resection of epileptogenic (epi) foci from multiple cortical foci determine surgical outcomes in the tuberous sclerosis compl...

How much EEG is needed for deep learning with convolutional neural networks? Predicting the benefit from additional data.

Objective.This study quantifies how the accuracy of convolutional neural networks for electroencephalogram (EEG) classification depends on the amount ...

Multi-scale kernel and electrode attention network for EEG-based epileptic seizure detection.

Epilepsy is a common chronic neurological disorder, and automated detection of epileptic seizures using multi-channel electroencephalography (EEG) is ...

Graph empirical mode decomposition and multiscale feature extraction for EEG-based classification of Alzheimer's disease and frontotemporal dementia.

BACKGROUND AND OBJECTIVE: Early and correct classification of neurodegenerative diseases like Alzheimer's Disease (AD) and Frontotemporal Dementia (FT...