Seizures

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...

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

Major depressive disorder (MDD) or depression is a chronic mental illness that significantly impacts...

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 cogni...

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 electroenc...

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 th...

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 sta...

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 el...

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

OBJECTIVE: Data augmentation is important for enhancing subject-independent classification in deep l...

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 reha...

Balancing noise reduction and neural signature preservation in EEG biometrics.

EEG-based subject identification is an emerging biometric approach with strong potential for secure ...

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 elo...

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 intrins...

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...

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 (...

EEG-based epileptic seizure prediction with patient-tailored spectral-spatial-temporal feature learning.

Epilepsy is a chronic brain disorder characterized by recurrent seizures resulting from abnormal bra...

Seizure forecasting with multiple timescales and features.

OBJECTIVE: Forecasting epileptic seizures is a difficult task. Studies of seizure prediction have in...

Comparing the cognitive-motor performance of individuals with temporal lobe epilepsy versus healthy controls using robotics.

Cognitive impairments are common in individuals with temporal lobe epilepsy (TLE). Interactive Kinar...

Dementia severity index: A threshold-based approach to classifying dementia levels using resting state EEG.

BACKGROUND: Alzheimer's Disease (AD) and FrontoTemporal Dementia (FTD) are dementia conditions that ...

LMSA-net: a lightweight multi-scale attention network for eeg-based emotion recognition.

Electroencephalogram (EEG)-based emotion recognition holds great potential in affective computing, m...

On-device single channel EEG classification on Android smartphones using lightweight machine learning models.

OBJECTIVE: Electroencephalogram (EEG) signals capture neuronal activity by measuring electrical acti...