Seizures

Predicting Human Intention-Behavior Through EEG Signal Analysis Using Multi-Scale CNN.

At present, the application of Electroencephalogram (EEG) signal classification to human intention-behavior prediction has become a hot topic in the brain computer interface (BCI) research field. In recent studies, the introduction of convolutional neural networks (CNN) has contributed to substantial improvements in the EEG signal classification performance. However, there is still a key challenge...

EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications.

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, ...

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 discovered. We propose a new method for predicting d...

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 towards replicating these processes in machines. The co...

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

. To identify a new electrophysiological feature characterising the epileptic seizures, which is commonly observed in different types of epilepsy.. We...

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-computer interfaces (BCI) that seeks communication...

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 brain dynamics. These signals are dynamic, complex, ...

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 combination of VR with electroencephalography (EEG) prese...

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) from raw electroencephalographic (EEG) signals whe...

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 negative consequences on health and safety, it is v...

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 electroencephalogram (EEG) signals is impleme...

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 of the auditory neural impairment may impose chal...

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 reflects surgical pain and state of the brain. Howe...

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 epilepsy (TLE) surgical candidates. To address thi...

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 through the infrequent occurrence of spontaneous seizur...

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 the quality and quantity of training data. Typically...

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 interact with the environment. To enable meaningful and ...

Fused CNN-LSTM deep learning emotion recognition model using electroencephalography signals.

The traditional machine learning-based emotion recognition models have shown effective performance for classifying Electroencephalography (EEG) based...

Systematic Review of the Effectiveness of Machine Learning Algorithms for Classifying Pain Intensity, Phenotype or Treatment Outcomes Using Electroencephalogram Data.

Recent attempts to utilize machine learning (ML) to predict pain-related outcomes from Electroencephalogram (EEG) data demonstrate promising results. ...