Neurology

Seizures

Latest AI and machine learning research in seizures for healthcare professionals.

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Wearable EEG Neurofeedback Based-on Machine Learning Algorithms for Children with Autism: A Randomized, Placebo-controlled Study.

OBJECTIVE: Behavioral interventions have been shown to ameliorate the electroencephalogram (EEG) dyn...

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 learn...

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

. Accurate classification of electroencephalogram (EEG) signals is crucial for advancing brain-compu...

Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology.

INTRODUCTION: Precise localization of the epileptogenic zone is critical for successful epilepsy sur...

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 adoptio...

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

Paroxysmal kinesigenic dyskinesia (PKD) is a rare neurological disorder marked by transient involunt...

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 manufac...

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 s...

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 o...

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 qu...

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...

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 epil...

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)....

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 utterance...

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 meanin...

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...

Anchoring temporal convolutional networks for epileptic seizure prediction.

. Accurate and timely prediction of epileptic seizures is crucial for empowering patients to mitigat...

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. Th...

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

In electroencephalogram (EEG) cognitive recognition research, the combined use of artificial neural ...

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,...

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