Seizures

Multiscale Spatial-Temporal Feature Fusion Neural Network for Motor Imagery Brain-Computer Interfaces.

Motor imagery, one of the main brain-computer interface (BCI) paradigms, has been extensively utilized in numerous BCI applications, such as the interaction between disabled people and external devices. Precise decoding, one of the most significant aspects of realizing efficient and stable interaction, has received a great deal of intensive research. However, the current decoding methods based on ...

Interpretable Multi-Branch Architecture for Spatiotemporal Neural Networks and Its Application in Seizure Prediction.

Currently, spatiotemporal convolutional neural networks (CNNs) for electroencephalogram (EEG) signals have emerged as promising tools for seizure prediction (SP), which explore the spatiotemporal biomarkers in an epileptic brain. Generally, these CNNs capture spatiotemporal features at single spectral resolution. However, epileptiform EEG signals contain irregular neural oscillations of different ...

An EEG-based emotion recognition method by fusing multi-frequency-spatial features under multi-frequency bands.

BACKGROUND: Recognition of emotion changes is of great significance to a person's physical and mental health. At present, EEG-based emotion recognitio...

A hybrid network using transformer with modified locally linear embedding and sliding window convolution for EEG decoding.

. Brain-computer interface(BCI) is leveraged by artificial intelligence in EEG signal decoding, which makes it possible to become a new means of human...

EEG Signals Classification Related to Visual Objects Using Long Short-Term Memory Network and Nonlinear Interval Type-2 Fuzzy Regression.

By gaining insights into how brain activity is encoded and decoded, we enhance our understanding of brain function. This study introduces a method for...

A hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection.

BACKGROUND: The diagnosis and treatment of epilepsy continue to face numerous challenges, highlighting the urgent need for the development of rapid, a...

Cognitive load detection through EEG lead wise feature optimization and ensemble classification.

Cognitive load stimulates neural activity, essential for understanding the brain's response to stress-inducing stimuli or mental strain. This study ex...

DHCT-GAN: Improving EEG Signal Quality with a Dual-Branch Hybrid CNN-Transformer Network.

Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity studies, cognitive mechanism research, and the di...

Leveraging deep learning for robust EEG analysis in mental health monitoring.

INTRODUCTION: Mental health monitoring utilizing EEG analysis has garnered notable interest due to the non-invasive characteristics and rich temporal ...

Detection of focal cortical dysplasia: Development and multicentric evaluation of artificial intelligence models.

OBJECTIVE: Focal cortical dysplasia (FCD) is a common cause of drug-resistant focal epilepsy but can be challenging to detect visually on magnetic res...

A Novel State Space Model with Dynamic Graphic Neural Network for EEG Event Detection.

Electroencephalography (EEG) is a widely used physiological signal to obtain information of brain activity, and its automatic detection holds signific...

Virtual reality-assisted prediction of adult ADHD based on eye tracking, EEG, actigraphy and behavioral indices: a machine learning analysis of independent training and test samples.

Given the heterogeneous nature of attention-deficit/hyperactivity disorder (ADHD) and the absence of established biomarkers, accurate diagnosis and ef...

Decoding of pain during heel lancing in human neonates with EEG signal and machine learning approach.

Currently, pain assessment using electroencephalogram signals and machine learning methods in clinical studies is of great importance, especially for ...

EEG-based emotion recognition using multi-scale dynamic CNN and gated transformer.

Emotions play a crucial role in human thoughts, cognitive processes, and decision-making. EEG has become a widely utilized tool in emotion recognition...

Emotion recognition using multi-scale EEG features through graph convolutional attention network.

Emotion recognition via electroencephalogram (EEG) signals holds significant promise across various domains, including the detection of emotions in pa...

A novel way to use cross-validation to measure connectivity by machine learning allows epilepsy surgery outcome prediction.

The rate of success of epilepsy surgery, ensuring seizure-freedom, is limited by the lack of epileptogenicity biomarkers. Previous evidence supports t...

Preictal period optimization for deep learning-based epileptic seizure prediction.

. Accurate seizure prediction could prove critical for improving patient safety and quality of life in drug-resistant epilepsy. While deep learning-ba...

Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism.

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate ch...

Multi-modal cross-domain self-supervised pre-training for fMRI and EEG fusion.

Neuroimaging techniques including functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) have shown promise in detecting function...

Multimodal data-based human motion intention prediction using adaptive hybrid deep learning network for movement challenged person.

Recently, social demands for a good quality of life have increased among the elderly and disabled people. So, biomedical engineers and robotic researc...