Seizures

Enhanced classification of tinnitus patients using EEG microstates and deep learning techniques.

This study aims to deepen the understanding and classification of tinnitus through a comprehensive analysis of EEG signals utilizing innovative microstate analysis techniques and cutting-edge machine learning approaches. EEG data were collected from two datasets: a primary dataset with 36 participants (16 healthy, 20 tinnitus) and a public dataset with 37 participants (15 healthy, 22 tinnitus). Si...

Event driven neural network on a mixed signal neuromorphic processor for EEG based epileptic seizure detection.

Long-term monitoring of biomedical signals is essential for the modern clinical management of neurological conditions such as epilepsy. However, developing wearable systems that are able to monitor, analyze, and detect epileptic seizures with long-lasting operation times using current technologies is still an open challenge. Brain-inspired spiking neural networks (SNNs) represent a promising signa...

A Distributed Neural Network Architecture for Dynamic Sensor Selection With Application to Bandwidth-Constrained Body-Sensor Networks.

We propose a dynamic sensor selection approach for deep neural networks (DNNs), which is able to derive an optimal sensor subset selection for each sp...

Unsupervised Domain Adaptation With Synchronized Self-Training for Cross- Domain Motor Imagery Recognition.

Robust decoding performance is essential for the practical deployment of brain-computer interface (BCI) systems. Existing EEG decoding models often re...

AI-driven early diagnosis of specific mental disorders: a comprehensive study.

One of the areas where artificial intelligence (AI) technologies are used is the detection and diagnosis of mental disorders. AI approaches, including...

Entropy-driven deep learning framework for epilepsy detection using electro encephalogram signals.

Epilepsy is one of the most frequently occurring neurological disorders that require early and accurate detection. This paper introduces a novel appro...

A Novel 3D Approach with a CNN and Swin Transformer for Decoding EEG-Based Motor Imagery Classification.

Motor imagery (MI) is a crucial research field within the brain-computer interface (BCI) domain. It enables patients with muscle or neural damage to c...

Ensemble Learning-Based Alzheimer's Disease Classification Using Electroencephalogram Signals and Clock Drawing Test Images.

Ensemble learning (EL), a machine learning technique that combines the results of multiple learning algorithms to obtain predicted values, aims to ach...

A depression detection approach leveraging transfer learning with single-channel EEG.

Major depressive disorder (MDD) is a widespread mental disorder that affects health. Many methods combining electroencephalography (EEG) with machine ...

Retraining and evaluation of machine learning and deep learning models for seizure classification from EEG data.

Electroencephalography (EEG) is one of the most used techniques to perform diagnosis of epilepsy. However, manual annotation of seizures in EEG data i...

Convolutional Dynamically Convergent Differential Neural Network for Brain Signal Classification.

The brain signal classification is the basis for the implementation of brain-computer interfaces (BCIs). However, most existing brain signal classific...

SMANet: A Model Combining SincNet, Multi-Branch Spatial-Temporal CNN, and Attention Mechanism for Motor Imagery BCI.

Building a brain-computer interface (BCI) based on motor imagery (MI) requires accurately decoding MI tasks, which poses a significant challenge due t...

Tiny Convolutional Neural Network with Supervised Contrastive Learning for Epileptic Seizure Prediction.

Automatic seizure prediction based on ElectroEncephaloGraphy (EEG) ensures the safety of patients with epilepsy and mitigates anxiety. In recent years...

Interictal network dysfunction and cognitive impairment in epilepsy.

Epilepsy is diagnosed when neural networks become capable of generating excessive or hypersynchronous activity patterns that result in observable seiz...

Demonstration of impaired facial emotion perception in temporal lobe epilepsy by theta responses in EEG.

OBJECTIVE: Temporale lobe and occipito-temporal cortical areas play an important role in facial emotion perception (FEP). FEP might be represented by ...

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 develo...

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 lear...

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...