Neurology

Seizures

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

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Artificial intelligence in epilepsy - applications and pathways to the clinic.

Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy h...

Deciphering seizure semiology in corpus callosum injuries: A comprehensive systematic review with machine learning insights.

INTRODUCTION: Seizure disorders have often been found to be associated with corpus callosum injuries...

EEG classification model for virtual reality motion sickness based on multi-scale CNN feature correlation.

BACKGROUND: Virtual reality motion sickness (VRMS) is a key issue hindering the development of virtu...

Detecting emotions through EEG signals based on modified convolutional fuzzy neural network.

Emotion is a human sense that can influence an individual's life quality in both positive and negati...

EEG-Based Mental Workload Classification Method Based on Hybrid Deep Learning Model Under IoT.

Automatically detecting human mental workload to prevent mental diseases is highly important. With t...

Multi-Task Heterogeneous Ensemble Learning-Based Cross-Subject EEG Classification Under Stroke Patients.

Robot-assisted motor training is applied for neurorehabilitation in stroke patients, using motor ima...

Assessing the effectiveness of spatial PCA on SVM-based decoding of EEG data.

Principal component analysis (PCA) has been widely employed for dimensionality reduction prior to mu...

Amplitude-Time Dual-View Fused EEG Temporal Feature Learning for Automatic Sleep Staging.

Electroencephalogram (EEG) plays an important role in studying brain function and human cognitive pe...

Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting.

The recent scientific literature abounds in proposals of seizure forecasting methods that exploit ma...

Emotion recognition with reduced channels using CWT based EEG feature representation and a CNN classifier.

Although emotion recognition has been studied for decades, a more accurate classification method tha...

Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images.

The treatment of epilepsy, the second most common chronic neurological disorder, is often complicate...

ZleepAnlystNet: a novel deep learning model for automatic sleep stage scoring based on single-channel raw EEG data using separating training.

Numerous models for sleep stage scoring utilizing single-channel raw EEG signal have typically emplo...

Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs.

Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer inter...

A hybrid 1D CNN-BiLSTM model for epileptic seizure detection using multichannel EEG feature fusion.

Epilepsy, a chronic non-communicable disease is characterized by repeated unprovoked seizures, which...

Attention-based convolutional neural network with multi-modal temporal information fusion for motor imagery EEG decoding.

Convolutional neural network (CNN) has been widely applied in motor imagery (MI)-based brain compute...

Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN-LSTM model.

BACKGROUND AND OBJECTIVE: People with autism spectrum disorder (ASD) often have cognitive impairment...

Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images.

Schizophrenia (SZ) has been acknowledged as a highly intricate mental disorder for a long time. In f...

Physics-Informed Transfer Learning to Enhance Sleep Staging.

OBJECTIVE: At-home sleep staging using wearable medical sensors poses a viable alternative to in-hos...

Classification of mental workload using brain connectivity and machine learning on electroencephalogram data.

Mental workload refers to the cognitive effort required to perform tasks, and it is an important fac...

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