Seizures

Diffusion Model-Based Multi-Channel EEG Representation and Forecasting for Early Epileptic Seizure Warning.

Predicting delirium in critically Ill COVID-19 patients using EEG-derived data: a machine learning approach.

Delirium is a severe and common complication among critically ill patients, particularly those with SARS-CoV-2 infection, contributing to increased morbidity and mortality. Early identification of at-risk patients is crucial for timely intervention and improved outcomes. This prospective observational cohort study explores the potential of electroencephalography (EEG) combined with machine learnin...

Neural correlates of evaluative bias against AI-labeled versus human-labeled artworks.

The rapid advancement of generative artificial intelligence (AI) has enabled machines to produce creative outputs, such as artworks, that rival human ...

Short-horizon neonatal seizure prediction using EEG-based deep learning.

Strategies to predict neonatal seizure risk have typically focused on long-term static predictions with prediction horizons spanning days during the a...

How EEG preprocessing shapes decoding performance.

Electroencephalography (EEG) preprocessing varies widely between studies, but its impact on classification performance remains poorly understood. To a...

An investigation of multimodal EMG-EEG fusion strategies for upper-limb gesture classification.

. Upper-limb gesture identification is an important problem in the advancement of robotic prostheses. Prevailing research into classifying electromyog...

A transformer-based network with second-order pooling for motor imagery EEG classification.

. Electroencephalography (EEG) signals can reflect motor intention signals in the brain. In recent years, motor imagery (MI) based brain-computer inte...

Brain region localization: a rapid Parkinson's disease detection method based on EEG signals.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder worldwide, often progressing to mild cognitive impairment (MCI) and dementia. Clini...

Detection of focal impaired awareness seizures using a biometric shirt.

OBJECTIVE: In recent years, seizure detection using wearable technology has gained significant attention in research. Most studies, however, have focu...

A Composable Channel-Adaptive Architecture for Seizure Classification.

Multi-variate time-series are one of the primary data modalities involved in large classes of problems, where deep learning models represent the state...

Insights into the Diagnosis, Treatment, and Management of Prion Diseases.

The normal cellular prion protein (PrPC) can misfold into an infectious and pathogenic form (PrPSc) to produce prion diseases, also known as transmiss...

Depression EEG classification based on multi-scale convolutional transformer network.

Depression electroencephalograph (EEG) classification based on machine learning is helpful for the auxiliary diagnosis of major depression disorder (M...

Motor imagery EEG signal classification using novel deep learning algorithm.

Electroencephalography (EEG) signal classification plays a critical role in various biomedical and cognitive research applications, including neurolog...

EEG Signal Analysis for the Classification of Alzheimer's and Frontotemporal Dementia: A Novel Approach Using Artificial Neural Networks and Cross-Entropy Techniques.

Dementia, a neurological disorder, can cause cognitive decline due to damage to the brain. Our study aims to contribute to the development of computer...

Optimal multimodal feature combination and classifier selection for music-based EEG signal analysis.

PURPOSE: Music perception is a fundamental human experience, integral to cognitive and emotional processing, making it a crucial area for neuroscienti...

Leveraging pathological markers of lower grade glioma to predict the occurrence of secondary epilepsy, a retrospective study.

Epilepsy is a common manifestation in patients with lower grade glioma (LGG), often presenting as the initial symptom in approximately 70% of cases. T...

Individualized structural network deviations predict surgical outcome in mesial temporal lobe epilepsy: a multicentre validation study.

BACKGROUND: Surgical resection is an effective treatment for medically refractory mesial temporal lobe epilepsy (mTLE), however, more than one-third o...

An EEG-based seizure prediction model encoding brain network temporal dynamics.

EEG-based seizure prediction enables timely treatment for patients, but its performance is limited by the difficulty in effectively characterizing the...

Artificial Intelligence and Machine Learning in Neuromodulation for Epilepsy.

Recent advances in artificial intelligence (AI) and machine learning (ML) can revolutionize neuromodulation therapies for drug-resistant epilepsy. Suc...

A novel ST-GCN model based on homologous microstate for subject-independent seizure prediction.

Due to the lack of validated universal seizure markers, population-level prediction methods often exhibit limited performance. This study proposes hom...