Seizures

TriNet-MTL: A Multi-Branch Deep Learning Framework for Biometric Identification and Cognitive State Inference from Auditory-Evoked EEG.

Auditory-evoked EEG signals contain rich temporal and cognitive features that reflect both the identity of individuals and their neural response to external stimuli. Traditional unimodal approaches often fail to fully leverage this multidimensional information fully, limiting their effectiveness in real-world biometric and neurocognitive applications. This study aims to develop a unified deep lear...

A knowledge-driven self-supervised learning method for enhancing EEG-based emotion recognition.

Emotion recognition brain-computer interface (BCI) using electroencephalography (EEG) is crucial for human-computer interaction, medicine, and neuroscience. However, the scarcity of labeled EEG data limits progress in this field. To address this, self-supervised learning has gained attention as a promising approach. Despite its potential, self-supervised methods face two key challenges: (1) ensuri...

Altered entropy modulation in bipolar disorder: EEG entropy measures during steady-state auditory entrainment.

BACKGROUND: Non-linear neural dynamics reflect the inherent complexity of brain activity and are increasingly recognized as important indicators of ne...

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 ...

Enhancing upper limb motor recovery prediction after acute stroke using EEG and subacute data.

Electroencephalography (EEG) has shown promise in assessing and monitoring functional recovery in stroke survivors, but its utility in predicting uppe...

Toward early warning of unsafe behavior of excavator operators under time pressure: experimental evidence and EEG-based detection via RCF-IncepLite model.

Time pressure can impair the cognitive functioning of excavator operators, thereby increasing unsafe behaviors and elevating the likelihood of acciden...

Simplifying Depression Diagnosis: Single-Channel EEG and Deep Learning Approaches.

Major depressive disorder (MDD) or depression is a chronic mental illness that significantly impacts individuals' well-being and is often diagnosed at...

AI-driven framework for accurate detection of Alzheimer's disease in EEG.

Alzheimer's Disease (AD) is a rapidly growing neurodegenerative disorder that severely impairs cognitive function, particularly among older adults. Ea...

AI-Driven Electrographic Seizure Classification and Seizure Onset Detection Using Image- and Time-Series-Based Approaches.

OBJECTIVE: Manually distinguishing between seizure and non-seizure events in intracranial electroencephalography (iEEG) recordings is highly time-cons...

Prediction of anti-epileptic drug response of patients based on peripheral blood RNA profiles and machine learning.

OBJECTIVE: In this study, we aimed to develop a method for predicting the response of patients to three commonly used anti-epileptic drugs (AEDs), nam...

SBTM: epileptic seizure prediction from EEG signal using deep learning in blockchain-enabled smart healthcare monitoring with IoT networking.

Distinct clinical clusters of paediatric patients with status epilepticus: Retrospective cohort study.

AIM: To characterize the clinical features, management, and outcomes of paediatric patients with status epilepticus, and to explore whether distinct c...

Real-world evaluation of an automated EEG spike detection software in a tertiary centre compared to a clinical reference standard.

BACKGROUND: Interictal epileptiform discharges (IEDs) are transient spikes or waves that occur in electroencephalography (EEG) records and can help su...

Data Augmentation for Subject-Independent SSVEP-BCIs via Simultaneous Spatial-Energy Representation.

OBJECTIVE: Data augmentation is important for enhancing subject-independent classification in deep learning (DL) approaches for steady-state visual ev...

Adversarial robust EEG-based brain-computer interfaces using a hierarchical convolutional neural network.

Brain-Computer Interfaces (BCIs) based on electroencephalography (EEG) are widely used in motor rehabilitation, assistive communication, and neurofeed...

Balancing noise reduction and neural signature preservation in EEG biometrics.

EEG-based subject identification is an emerging biometric approach with strong potential for secure authentication, but reliable performance requires ...

ESM-AnatTractNet: Advanced deep learning model of true positive eloquent white matter tractography to improve preoperative evaluation of pediatric epilepsy surgery.

Accurate preoperative identification of true positive white matter pathways involved in critical eloquent functions such as motor, language, and visio...

Spatiotemporal abnormalities in brain networks as a signature of neurological damage in Wilson's disease.

OBJECTIVES: Resting-state electroencephalogram (EEG) microstates serve as dynamic markers of intrinsic brain activity, reflecting the transient coordi...

Multiple epileptiform waves detection algorithm based on improved VMD and multidimensional feature fusion.

BACKGROUND: Spikes, ripples, and ripples on spikes (RonS) during non-rapid eye movement (NREM) sleep are all important biomarkers associated with epil...

Seizure risk prediction using machine learning following glioma resection surgery in seizure-naïve patients.

BACKGROUND: Despite the ongoing controversy around the prophylactic use of antiseizure medications (ASMs) in seizure-naïve patients undergoing brain t...