Seizures

Model-agnostic meta-learning for EEG-based inter-subject emotion recognition.

. Developing an efficient and generalizable method for inter-subject emotion recognition from neural signals is an emerging and challenging problem in affective computing. In particular, human subjects usually have heterogeneous neural signal characteristics and variable emotional activities that challenge the existing recognition algorithms from achieving high inter-subject emotion recognition ac...

Machine Learning-Based Diagnosis of Chronic Subjective Tinnitus With Altered Cognitive Function: An Event-Related Potential Study.

OBJECTIVES: Due to the absence of objective diagnostic criteria, tinnitus diagnosis primarily relies on subjective assessments. However, its neuropathological features can be objectively quantified using electroencephalography (EEG). Despite the existing research, the pathophysiology of tinnitus remains unclear. The objective of this study was to gain a deeper comprehension of the neural mechanism...

Alzheimer's disease diagnosis using rhythmic power changes and phase differences: a low-density EEG study.

OBJECTIVES: The future emergence of disease-modifying treatments for dementia highlights the urgent need to identify reliable and easily accessible to...

Diagnosing Epilepsy with Normal Interictal EEG Using Dynamic Network Models.

OBJECTIVE: Whereas a scalp electroencephalogram (EEG) is important for diagnosing epilepsy, a single routine EEG is limited in its diagnostic value. O...

Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification.

In recent years, the utilization of motor imagery (MI) signals derived from electroencephalography (EEG) has shown promising applications in controlli...

Working-memory load decoding model inspired by brain cognition based on cross-frequency coupling.

Working memory, a fundamental cognitive function of the brain, necessitates the evaluation of cognitive load intensity due to limited cognitive resour...

Screening of Aβ and phosphorylated tau status in the cerebrospinal fluid through machine learning analysis of portable electroencephalography data.

Diagnosing Alzheimer's disease (AD) through pathological markers is typically costly and invasive. This study aims to find a noninvasive, cost-effecti...

Supervised Contrastive Learning-Based Domain Generalization Network for Cross-Subject Motor Decoding.

Developing an electroencephalogram (EEG)-based motor imagery and motor execution (MI/ME) decoding system that is both highly accurate and calibration-...

Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning.

Major depressive disorder (MDD) is associated with substantial morbidity and mortality, yet its diagnosis and treatment rates remain low due to its di...

A Fine-grained Hemispheric Asymmetry Network for accurate and interpretable EEG-based emotion classification.

In this work, we propose a Fine-grained Hemispheric Asymmetry Network (FG-HANet), an end-to-end deep learning model that leverages hemispheric asymmet...

Utilizing natural language processing to identify pediatric patients experiencing status epilepticus.

PURPOSE: Compare the identification of patients with established status epilepticus (ESE) and refractory status epilepticus (RSE) in electronic health...

EEG microstate analysis and machine learning classification in patients with obsessive-compulsive disorder.

BACKGROUND: Microstate characterization of electroencephalogram (EEG) is a data-driven approach to explore the functional changes and interrelationshi...

MFRC-Net: Multi-Scale Feature Residual Convolutional Neural Network for Motor Imagery Decoding.

Motor imagery (MI) decoding is the basis of external device control via electroencephalogram (EEG). However, the majority of studies prioritize enhanc...

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 inter...

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 pred...

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...