Seizures

Brain-computer interface for robot control with eye artifacts for assistive applications.

Human-robot interaction is a rapidly developing field and robots have been taking more active roles in our daily lives. Patient care is one of the fields in which robots are becoming more present, especially for people with disabilities. People with neurodegenerative disorders might not consciously or voluntarily produce movements other than those involving the eyes or eyelids. In this context, Br...

Detection and classification of adult epilepsy using hybrid deep learning approach.

The electroencephalogram (EEG) has emerged over the past few decades as one of the key tools used by clinicians to detect seizures and other neurological abnormalities of the human brain. The proper diagnosis of epilepsy is crucial due to its distinctive nature and the subsequent negative effects of epileptic seizures on patients. The classification of minimally pre-processed, raw multichannel EEG...

Deep Learning-Based Classification of Epileptic Electroencephalography Signals Using a Concentrated Time-Frequency Approach.

ConceFT (concentration of frequency and time) is a new time-frequency (TF) analysis method which combines multitaper technique and synchrosqueezing tr...

Electroencephalographic abnormalities in children with type 1 diabetes mellitus: a prospective study.

BACKGROUND/AIM: The aim herein was to investigate epileptiform discharges on electroencephalogram (EEG), their correlation with glutamic acid decarbox...

Evaluation of a novel real-time adaptive assist-as-needed controller for robot-assisted upper extremity rehabilitation following stroke.

Rehabilitation therapy plays an essential role in assisting people with stroke regain arm function. Upper extremity robot therapy offers a number of a...

Bayesian learning from multi-way EEG feedback for robot navigation and target identification.

Many brain-computer interfaces require a high mental workload. Recent research has shown that this could be greatly alleviated through machine learnin...

EEG-Based Cross-Subject Driver Drowsiness Recognition With an Interpretable Convolutional Neural Network.

In the context of electroencephalogram (EEG)-based driver drowsiness recognition, it is still challenging to design a calibration-free system, since E...

Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals.

Mild cognitive impairment (MCI) is a neuropsychological syndrome that is characterized by cognitive impairments. It typically affects adults 60 years ...

An ensemble deep-learning approach for single-trial EEG classification of vibration intensity.

. Single-trial electroencephalography (EEG) classification is a promising approach to evaluate the cognitive experience associated with haptic feedbac...

Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach.

A large share of traffic accidents is related to driver fatigue. In recent years, many studies have been organized in order to diagnose and warn drive...

Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia.

(SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect appr...

Applications for Deep Learning in Epilepsy Genetic Research.

Epilepsy is a group of brain disorders characterised by an enduring predisposition to generate unprovoked seizures. Fuelled by advances in sequencing ...

A Deep Learning Model for Correlation Analysis between Electroencephalography Signal and Speech Stimuli.

In recent years, the use of electroencephalography (EEG) has grown as a tool for diagnostic and brain function monitoring, being a simple and non-inva...

Overview of methods and available tools used in complex brain disorders.

Complex brain disorders, including Alzheimer's dementia, sleep disorders, and epilepsy, are chronic conditions that have high prevalence individually ...

Brain-guided manifold transferring to improve the performance of spiking neural networks in image classification.

Spiking neural networks (SNNs), as the third generation of neural networks, are based on biological models of human brain neurons. In this work, a sha...

Deep learning based source imaging provides strong sublobar localization of epileptogenic zone from MEG interictal spikes.

Electromagnetic source imaging (ESI) offers unique capability of imaging brain dynamics for studying brain functions and aiding the clinical managemen...

Machine learning and EEG can classify passive viewing of discrete categories of visual stimuli but not the observation of pain.

Previous studies have demonstrated the potential of machine learning (ML) in classifying physical pain from non-pain states using electroencephalograp...

Emotion recognition in EEG signals using deep learning methods: A review.

Emotions are a critical aspect of daily life and serve a crucial role in human decision-making, planning, reasoning, and other mental states. As a res...

The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG.

Common misbehavior among children that prevents them from paying attention to tasks and interacting with their surroundings appropriately is attention...

Automatic identification of schizophrenia employing EEG records analyzed with deep learning algorithms.

Electroencephalography is a method of detecting and analyzing electrical activity in the brain. This electrical activity can be recorded and processed...