Seizures

Classification of hand movements from EEG using a FusionNet based LSTM network.

. Accurate classification of electroencephalogram (EEG) signals is crucial for advancing brain-computer interface (BCI) technology. However, current methods face significant challenges in classifying hand movement EEG signals, including effective spatial feature extraction, capturing temporal dependencies, and representing underlying signal dynamics.. This paper introduces a novel multi-model fusi...

A continuous pursuit dataset for online deep learning-based EEG brain-computer interface.

This dataset is from an EEG brain-computer interface (BCI) study investigating the use of deep learning (DL) for online continuous pursuit (CP) BCI. In this task, subjects use Motor Imagery (MI) to control a cursor to follow a randomly moving target, instead of a single stationary target used in other traditional BCI tasks. DL methods have recently achieved promising performance in traditional BCI...

Deep Learning Recognition of Paroxysmal Kinesigenic Dyskinesia Based on EEG Functional Connectivity.

Paroxysmal kinesigenic dyskinesia (PKD) is a rare neurological disorder marked by transient involuntary movements triggered by sudden actions. Current...

Enhancing Motor Imagery Classification with Residual Graph Convolutional Networks and Multi-Feature Fusion.

Stroke, an abrupt cerebrovascular ailment resulting in brain tissue damage, has prompted the adoption of motor imagery (MI)-based brain-computer inter...

Assessing operator stress in collaborative robotics: A multimodal approach.

In the era of Industry 4.0, the study of Human-Robot Collaboration (HRC) in advancing modern manufacturing and automation is paramount. An operator ap...

Partial prior transfer learning based on self-attention CNN for EEG decoding in stroke patients.

The utilization of motor imagery-based brain-computer interfaces (MI-BCI) has been shown to assist stroke patients activate motor regions in the brain...

Predictive models for secondary epilepsy in patients with acute ischemic stroke within one year.

BACKGROUND: Post-stroke epilepsy (PSE) is a critical complication that worsens both prognosis and quality of life in patients with ischemic stroke. An...

Analysis of the impact of deep learning know-how and data in modelling neonatal EEG.

The performance gains achieved by deep learning models nowadays are mainly attributed to the usage of ever larger datasets. In this study, we present ...

Expert level of detection of interictal discharges with a deep neural network.

OBJECTIVE: Deep learning methods have shown potential in automating the detection of interictal epileptiform discharges (IEDs) in electroencephalograp...

Recognizing and explaining driving stress using a Shapley additive explanation model by fusing EEG and behavior signals.

Driving stress is a critical factor leading to road traffic accidents. Despite numerous studies that have been conducted on driving stress recognition...

Predicting the Risk of Driving Under the Influence of Alcohol Using EEG-Based Machine Learning.

Driving under the influence of alcohol (DUIA) is closely associated with alcohol use disorder (AUD). Our previous study on machine learning (ML) algor...

Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation.

To transform continuous speech into words, the human brain must resolve variability across utterances in intonation, speech rate, volume, accents and ...

QuadTPat: Quadruple Transition Pattern-based explainable feature engineering model for stress detection using EEG signals.

The most cost-effective data collection method is electroencephalography (EEG), which obtains meaningful information about the brain. Therefore, EEG s...

Anchoring temporal convolutional networks for epileptic seizure prediction.

. Accurate and timely prediction of epileptic seizures is crucial for empowering patients to mitigate their impact or prevent them altogether. Current...

A hybrid local-global neural network for visual classification using raw EEG signals.

EEG-based brain-computer interfaces (BCIs) have the potential to decode visual information. Recently, artificial neural networks (ANNs) have been used...

Depression diagnosis: EEG-based cognitive biomarkers and machine learning.

Depression is a complex mental illness that has significant effects on people as well as society. The traditional techniques for the diagnosis of depr...

A General DNA-Like Hybrid Symbiosis Framework: An EEG Cognitive Recognition Method.

In electroencephalogram (EEG) cognitive recognition research, the combined use of artificial neural networks (ANNs) and spiking neural networks (SNNs)...

Task-oriented EEG denoising generative adversarial network for enhancing SSVEP-BCI performance.

The quality of electroencephalogram (EEG) signals directly impacts the performance of brain-computer interface (BCI) tasks. Many methods have been pro...

Classification of EEG evoked in 2D and 3D virtual reality: traditional machine learning versus deep learning.

. Virtual reality (VR) simulates real-life events and scenarios and is widely utilized in education, entertainment, and medicine. VR can be presented ...

Machine learning for forecasting initial seizure onset in neonatal hypoxic-ischemic encephalopathy.

OBJECTIVE: This study was undertaken to develop a machine learning (ML) model to forecast initial seizure onset in neonatal hypoxic-ischemic encephalo...