Seizures

A Multi-Scale Activity Transition Network for Data Translation in EEG Signals Decoding.

Electroencephalogram (EEG) is a non-invasive collection method for brain signals. It has broad prosp...

Subject-Independent Emotion Recognition of EEG Signals Based on Dynamic Empirical Convolutional Neural Network.

Affective computing is one of the key technologies to achieve advanced brain-machine interfacing. It...

Advanced Machine-Learning Methods for Brain-Computer Interfacing.

The brain-computer interface (BCI) connects the brain and the external world through an information ...

A Hierarchical Discriminative Sparse Representation Classifier for EEG Signal Detection.

Classification of electroencephalogram (EEG) signal data plays a vital role in epilepsy detection. R...

Epilepsy Signal Recognition Using Online Transfer TSK Fuzzy Classifier Underlying Classification Error and Joint Distribution Consensus Regularization.

In this study, an online transfer TSK fuzzy classifier O-T-TSK-FC is proposed for recognizing epilep...

Prediction of GABA receptor antagonist-induced convulsion in cynomolgus monkeys by combining machine learning and heart rate variability analysis.

Drug-induced convulsion is a severe adverse event; however, no useful biomarkers for it have been di...

Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features.

This work presents a novel method of exploring human brain-visual representations, with a view towar...

Data-driven electrophysiological feature based on deep learning to detect epileptic seizures.

. To identify a new electrophysiological feature characterising the epileptic seizures, which is com...

Recognition of EEG Signals from Imagined Vowels Using Deep Learning Methods.

The use of imagined speech with electroencephalographic (EEG) signals is a promising field of brain-...

Effect of combining features generated through non-linear analysis and wavelet transform of EEG signals for the diagnosis of encephalopathy.

Electroencephalogram (EEG) signals portray hidden neuronal interactions in the brain and indicate br...

Predicting motor behavior: an efficient EEG signal processing pipeline to detect brain states with potential therapeutic relevance for VR-based neurorehabilitation.

Virtual reality (VR)-based motor therapy is an emerging approach in neurorehabilitation. The combina...

Optimizing Motor Intention Detection With Deep Learning: Towards Management of Intraoperative Awareness.

OBJECTIVE: This article shows the interest in deep learning techniques to detect motor imagery (MI) ...

EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features.

Exposure to mental stress for long period leads to serious accidents and health problems. To avoid n...

EEG-Based Personality Prediction Using Fast Fourier Transform and DeepLSTM Model.

In this paper, a deep long short term memory (DeepLSTM) network to classify personality traits using...

The emergence of machine learning in auditory neural impairment: A systematic review.

Hearing loss is a common neurodegenerative disease that can start at any stage of life. Misalignment...

Mapping Epileptogenic Tissues in MRI-Negative Focal Epilepsy: Can Deep Learning Uncover Hidden Lesions?

A Combinatorial Deep Learning Structure for Precise Depth of Anesthesia Estimation From EEG Signals.

Electroencephalography (EEG) is commonly used to measure the depth of anesthesia (DOA) because EEG r...

MRI-Based Machine Learning Prediction Framework to Lateralize Hippocampal Sclerosis in Patients With Temporal Lobe Epilepsy.

BACKGROUND AND OBJECTIVES: MRI fails to reveal hippocampal pathology in 30% to 50% of temporal lobe ...

Deep learning based smart health monitoring for automated prediction of epileptic seizures using spectral analysis of scalp EEG.

Being one of the most prevalent neurological disorders, epilepsy affects the lives of patients throu...

Generative Adversarial Networks-Based Data Augmentation for Brain-Computer Interface.

The performance of a classifier in a brain-computer interface (BCI) system is highly dependent on th...

Deep learning multimodal fNIRS and EEG signals for bimanual grip force decoding.

Non-invasive brain-machine interfaces (BMIs) offer an alternative, safe and accessible way to intera...