Seizures

Current and Emerging Precision Therapies for Developmental and Epileptic Encephalopathies.

Developmental and epileptic encephalopathies (DEEs) are severe neurological disorders characterized by childhood-onset seizures and significant developmental impairments. Seizures are often refractory to treatment with traditional antiseizure medications, which fail to address the underlying genetic and molecular mechanisms. This comprehensive review explores the evolving landscape of precision th...

FusionXNet: enhancing EEG-based seizure prediction with integrated convolutional and Transformer architectures.

. Effective seizure prediction can reduce patient burden, improve clinical treatment accuracy, and lower healthcare costs. However, existing deep learning-based seizure prediction methods primarily rely on single models, which have limitations in feature extraction. This study aims to develop a hybrid model that integrates the advantages of convolutional neural networks (CNNs) and Transformer to e...

TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments.

Semantic understanding is central to advanced cognitive functions, and the mechanisms by which the brain processes language information are still bein...

Enhanced EEG-based Alzheimer's disease detection using synchrosqueezing transform and deep transfer learning.

The most prevalent type of dementia and a progressive neurodegenerative disease, Alzheimer's disease has a major influence on day-to-day functioning d...

EEG-based epilepsy detection using CNN-SVM and DNN-SVM with feature dimensionality reduction by PCA.

This study focuses on epilepsy detection using hybrid CNN-SVM and DNN-SVM models, combined with feature dimensionality reduction through PCA. The goal...

SS-EMERGE - self-supervised enhancement for multidimension emotion recognition using GNNs for EEG.

Self-supervised learning (SSL) is a potent method for leveraging unlabelled data. Nonetheless, EEG signals, characterised by their low signal-to-noise...

Decoding of lexical items and grammatical features in EEG: A cross-linguistic study.

Diverse evidence supports the theory that bilingual language users have language-invariant representations of concepts and grammatical forms such as a...

Challenges and Opportunities: Nanomaterials in Epilepsy Diagnosis.

Epilepsy is a common neurological disorder characterized by a significant rate of disability. Accurate early diagnosis and precise localization of the...

HATNet: EEG-Based Hybrid Attention Transfer Learning Network for Train Driver State Detection.

Electroencephalography (EEG) is widely utilized for train driver state detection due to its high accuracy and low latency. However, existing methods f...

Dynamic Hierarchical Convolutional Attention Network for Recognizing Motor Imagery Intention.

The neural activity patterns of localized brain regions are crucial for recognizing brain intentions. However, existing electroencephalogram (EEG) dec...

AdamGraph: Adaptive Attention-Modulated Graph Network for EEG Emotion Recognition.

The underlying time-variant and subject-specific brain dynamics lead to inconsistent distributions in electroencephalogram (EEG) topology and represen...

Epileptic seizure detection in EEG signals using deep learning: LSTM and bidirectional LSTM.

This paper established a new automatic method to detect epileptic seizures in EEG signals based on discret wavelet transform (DWT) and Deep Learning (...

Initial seizure episodes risk factors identification during hospitalization of ICU patients: A retrospective analysis of the eICU collaborative research database.

BACKGROUND: We aimed to identify risk factors for initial seizure episodes in ICU patients using various machine learning algorithms.

Integrative neurorehabilitation using brain-computer interface: From motor function to mental health after stroke.

Stroke remains a leading cause of mortality and long-term disability worldwide, frequently resulting in impairments in motor control, cognition, and e...

Transcriptomic analyses of human brains with Alzheimer's disease identified dysregulated epilepsy-causing genes.

BACKGROUND & OBJECTIVE: Alzheimer's Disease (AD) patients at multiple stages of disease progression have a high prevalence of seizures. However, wheth...

Deep learning-based EEG source imaging is robust under varying electrode configurations.

OBJECTIVES: Previous research has underscored the necessity of high-density EEG for accurate and reliable EEG source imaging (ESI) results with conven...

Predicting Intraoperative Burst Suppression Using Preoperative EEG and Patient Characteristics.

Burst suppression (BS) is an electroencephalogram (EEG) pattern observed in patients undergoing general anesthesia. The occurrence of BS is associated...

Multi-scale convolutional transformer network for motor imagery brain-computer interface.

Brain-computer interface (BCI) systems allow users to communicate with external devices by translating neural signals into real-time commands. Convolu...

Leveraging pretrained language models for seizure frequency extraction from epilepsy evaluation reports.

Seizure frequency is essential for evaluating epilepsy treatment, ensuring patient safety, and reducing risk for Sudden Unexpected Death in Epilepsy. ...

AADNet: Exploring EEG Spatiotemporal Information for Fast and Accurate Orientation and Timbre Detection of Auditory Attention Based on a Cue-Masked Paradigm.

Auditory attention decoding from electroencephalogram (EEG) could infer to which source the user is attending in noisy environments. Decoding algorith...