Seizures

An Explainable Transfer Learning Method for EEG-based Seizure Type Classification.

Epilepsy, traditionally conceptualized as a neurological disorder characterized by a persistent inclination toward epileptic seizures, is commonly diagnosed and monitored through EEGs. However, manual analysis of EEG data can be exceedingly time-consuming. The integration of automated seizure classification methods represents a valuable resource for clinicians engaged in epilepsy analysis. In this...

Bi-hemisphere Interaction Convolutional Neural Network for Motor Imagery Classification.

Decoding EEG-based, Motor Imagery Brain-Computer Interfaces (MI-BCI) in a subject-independent manner is very challenging due to high dimensionality of the EEG signal, and high inter-subject variability. In recent years, Convolutional neural networks (CNNs) have significantly enhanced decoding accuracy. Nevertheless, the majority of these CNN designs did not explicitly incorporate the inter-hemisph...

Transformer-Based Wavelet-Scalogram Deep Learning for Improved Seizure Pattern Recognition in Post-Hypoxic-Ischemic Fetal Sheep EEG.

Hypoxic-ischemic (HI) events in newborns can trigger seizures, which are highly associated with later neurodevelopmental impairment. The precise detec...

Enhancing Epileptic Seizure Detection with Random Input Selection in Graph-Wave Networks.

Graph neural networks show strong capability of learning spatial relationships between channels. In recent studies, they greatly advanced automatic ep...

LightIED: Explainable AI with Light CNN for Interictal Epileptiform Discharge Detection.

Interictal epileptic discharge (IED) detection from electroencephalography (EEG) is an important but difficult step in the epilepsy diagnosis. To redu...

Reconstruction of Continuous Hand Grasp Movement from EEG Using Deep Learning.

Brain-Computer Interface (BCI) is a promising neu-rotechnology offering non-muscular control of external devices, such as neuroprostheses and robotic ...

Machine Learning Approach for Music Familiarity Classification with Single-Channel EEG.

Recognition of familiar music on brainwaves through machine learning (ML) can be instrumental in innovative therapeutic devices that improve memory an...

Detecting Post-Stroke Aphasia Via Brain Responses to Speech in a Deep Learning Framework.

Aphasia, a language disorder primarily caused by a stroke, is traditionally diagnosed using behavioral language tests. However, these tests are time-c...

Enhancing sleep stage classification with 2-class stratification and permutation-based channel selection.

We present a method that uses a convolutional neural network (CNN) called EEGNeX to extract and classify the characteristics of sleep-related waveform...

Automated Intraoperative Visual Detection of Pediatric Epileptogenic Brain Lesions Using a Machine Learning Classifier.

450,000 children with epilepsy in the United States suffer lifelong disability and are at risk of sudden death. Surgical treatment of epilepsy is limi...

Identifying Reproducibly Important EEG Markers of Schizophrenia with an Explainable Multi-Model Deep Learning Approach.

The diagnosis of schizophrenia (SZ) can be challenging due to its diverse symptom presentation. As such, many studies have sought to identify diagnost...

Resource-Efficient Continual Learning for Personalized Online Seizure Detection.

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant c...

Deep Residual Neural Networks for Spatial EEG Source Imaging.

EEG source imaging is an indispensable tool for non-invasive study of brain function. Existing methods mainly directly deal with the EEG inverse probl...

EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network.

Electroencephalogram (EEG) is a valuable technique to record brain electrical activity through electrodes placed on the scalp. Analyzing EEG signals c...

Diagnosing Suicidal Ideation from Resting State EEG Data Using a Machine Learning Algorithm.

Suicide poses a global health crisis with significant social and economic impact. Prevention may be possible if objective quantitative methods are dev...

Interictal Epileptiform Discharge Detection Using Time-Frequency Analysis and Transfer Learning.

Interictal epileptiform discharges (IEDs) are electrophysiological events that intermittently occur in between seizures in Epilepsy patients. Automate...

Baseline-Guided Representation Learning for Noise-Robust EEG Signal Classification.

Brain-computer interfaces (BCIs) suffer from limited accuracy due to noisy electroencephalography (EEG) signals. Existing denoising methods often remo...

HRV-based Monitoring of Neonatal Seizures with Machine Learning.

With the rapid development of machine learning (ML) in biomedical signal processing, ML-based neonatal seizure detection using heart rate variability ...

Domain-Incremental Learning Framework for Continual Motor Imagery EEG Classification Task.

Due to inter-subject variability in electroencephalogram (EEG) signals, the generalization ability of many existing brain-computer interface (BCI) mod...

Brain states analysis of EEG predicts multiple sclerosis and mirrors disease duration and burden

Background: Any treatment of multiple sclerosis should preserve mental function, considering how cognitive deterioration interferes with quality of ...