Seizures

Robot-assisted stereoelectroencephalography in young children: technical challenges and considerations.

Robot-assisted stereoelectroencephalography (sEEG) is frequently employed to localize epileptogenic zones in patients with medically refractory epilepsy (MRE). Its methodology is well described in adults, but less so in children. Given the limited information available on pediatric applications, the objective is to describe the unique technical challenges and considerations of sEEG in the pediatri...

AiED: Artificial intelligence for the detection of intracranial interictal epileptiform discharges.

OBJECTIVE: Deep learning provides an appealing solution for the ongoing challenge of automatically classifying intracranial interictal epileptiform discharges (IEDs). We report results from an automated method consisting of a template-matching algorithm and convolutional neural network (CNN) for the detection of intracranial IEDs ("AiED").

Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis.

Objective Dyslexia diagnosis is a challenging task, since traditional diagnosis methods are not based on biological markers but on behavioural tests. ...

Causal decoding of individual cortical excitability states.

Brain responsiveness to stimulation fluctuates with rapidly shifting cortical excitability state, as reflected by oscillations in the electroencephalo...

Brain oscillatory correlates of visuomotor adaptive learning.

Sensorimotor adaptation involves the recalibration of the mapping between motor command and sensory feedback in response to movement errors. Although ...

A Deep Learning-Based Classification Method for Different Frequency EEG Data.

In recent years, the research on electroencephalography (EEG) has focused on the feature extraction of EEG signals. The development of convenient and ...

Brain functional and effective connectivity based on electroencephalography recordings: A review.

Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between corti...

Effect of Lamotrigine on Ouabain-Induced Arrhythmia in Isolated Atria of Guinea Pigs.

Lamotrigine (LTG) is an antiepileptic drug used in the treatment of seizures, mood disorders, and cognitive problems. The cardiac effects of LTG, suc...

Evaluating Performance of EEG Data-Driven Machine Learning for Traumatic Brain Injury Classification.

OBJECTIVES: Big data analytics can potentially benefit the assessment and management of complex neurological conditions by extracting information that...

EEGdenoiseNet: a benchmark dataset for deep learning solutions of EEG denoising.

Deep learning (DL) networks are increasingly attracting attention across various fields, including electroencephalography (EEG) signal processing. The...

Applying machine learning EEG signal classification to emotion‑related brain anticipatory activity.

Machine learning approaches have been fruitfully applied to several neurophysiological signal classification problems. Considering the relevance of em...

A Driving Performance Forecasting System Based on Brain Dynamic State Analysis Using 4-D Convolutional Neural Networks.

Vehicle accidents are the primary cause of fatalities worldwide. Most often, experiencing fatigue on the road leads to operator errors and behavioral ...

Emotion Recognition Based on EEG Using Generative Adversarial Nets and Convolutional Neural Network.

Emotion recognition plays an important role in the field of human-computer interaction (HCI). Automatic emotion recognition based on EEG is an importa...

Subject-Specific Cognitive Workload Classification Using EEG-Based Functional Connectivity and Deep Learning.

Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other real-time and high-risk situations. Neuroimaging technique...

Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques.

Many studies report predictions for cognitive function but there are few predictions in epileptic patients; therefore, we established a workflow to ef...

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 epilepsy signals. Compared with most of the existing tra...

A Hierarchical Discriminative Sparse Representation Classifier for EEG Signal Detection.

Classification of electroencephalogram (EEG) signal data plays a vital role in epilepsy detection. Recently sparse representation-based classification...

Advanced Machine-Learning Methods for Brain-Computer Interfacing.

The brain-computer interface (BCI) connects the brain and the external world through an information transmission channel by interpreting the physiolog...

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 is increasingly concerning research orientation i...

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 prospects in brain-computer interface (BCI) application...