Seizures

Differentiating ischemic stroke patients from healthy subjects using a large-scale, retrospective EEG database and machine learning methods.

OBJECTIVES: We set out to develop a machine learning model capable of distinguishing patients presenting with ischemic stroke from a healthy cohort of subjects. The model relies on a 3-min resting electroencephalogram (EEG) recording from which features can be computed.

Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework.

Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-signal neuromorphic device to process the ECoG in real-time. We exploit the variability of the inhomogeneous silicon neurons to achieve efficient sparse...

vEpiNet: A multimodal interictal epileptiform discharge detection method based on video and electroencephalogram data.

To enhance deep learning-based automated interictal epileptiform discharge (IED) detection, this study proposes a multimodal method, vEpiNet, that lev...

Explainable Deep-Learning Prediction for Brain-Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion.

Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major ch...

An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network.

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life of over 10 million individuals worldwide. Early dia...

Enhancing cross-subject EEG emotion recognition through multi-source manifold metric transfer learning.

Transfer learning (TL) has demonstrated its efficacy in addressing the cross-subject domain adaptation challenges in affective brain-computer interfac...

Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment.

Anaesthesia, crucial to surgical practice, is undergoing renewed scrutiny due to the integration of artificial intelligence in its medical use. The pr...

Manifold Learning-Based Common Spatial Pattern for EEG Signal Classification.

EEG signal classification using Riemannian manifolds has shown great potential. However, the huge computational cost associated with Riemannian metric...

Development and prospective clinical validation of a convolutional neural network for automated detection and segmentation of focal cortical dysplasias.

PURPOSE: Focal cortical dysplasias (FCDs) are a leading cause of drug-resistant epilepsy. Early detection and resection of FCDs have favorable prognos...

Data-driven normative values based on generative manifold learning for quantitative MRI.

In medicine, abnormalities in quantitative metrics such as the volume reduction of one brain region of an individual versus a control group are often ...

Predicting an EEG-Based hypnotic time estimation with non-linear kernels of support vector machine algorithm.

Our ability to measure time is vital for daily life, technology use, and even mental health; however, separating pure time perception from other menta...

A machine learning based depression screening framework using temporal domain features of the electroencephalography signals.

Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely and precise recognition of depression is vital for ...

Applying Common Spatial Pattern and Convolutional Neural Network to Classify Movements via EEG Signals.

Developing an electroencephalography (EEG)-based brain-computer interface (BCI) system is crucial to enhancing the control of external prostheses by a...

Deep learning approaches for seizure video analysis: A review.

Seizure events can manifest as transient disruptions in the control of movements which may be organized in distinct behavioral sequences, accompanied ...

The promise of AI Large Language Models for Epilepsy care.

Artificial intelligence (AI) has been supporting our digital life for decades, but public interest in this has exploded with the recognition of large ...

A hybrid EEG classification model using layered cascade deep learning architecture.

The problem of multi-class classification is always a challenge in the field of EEG (electroencephalogram)-based seizure detection. The traditional st...

eDeeplepsy: An artificial neural framework to reveal different brain states in children with epileptic spasms.

OBJECTIVE: Despite advances, analysis and interpretation of EEG still essentially rely on visual inspection by a super-specialized physician. Consider...

Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human-robot collaboration.

. Brain-computer interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment whe...

PMF-CNN: parallel multi-band fusion convolutional neural network for SSVEP-EEG decoding.

Steady-state visual evoked potential (SSVEP) is a key technique of electroencephalography (EEG)-based brain-computer interfaces (BCI), which has been ...

Detecting cognitive traits and occupational proficiency using EEG and statistical inference.

Machine learning (ML) is widely used in classification tasks aimed at detecting various cognitive states or neurological diseases using noninvasive el...