Results 331 to 340 of about 874,261 (376)
Some of the next articles are maybe not open access.
2019
Indications for video-EEG monitoring (VEM) include differential diagnosis of paroxysmal events including epileptic seizures, organic nonepileptic seizures, and psychogenic nonepileptic seizures; classification of seizure types and electroclinical syndromes; quantification of seizures and of interictal and ictal epileptiform discharges; and presurgical ...
Christoph, Baumgartner, Susanne, Pirker
openaire +2 more sources
Indications for video-EEG monitoring (VEM) include differential diagnosis of paroxysmal events including epileptic seizures, organic nonepileptic seizures, and psychogenic nonepileptic seizures; classification of seizure types and electroclinical syndromes; quantification of seizures and of interictal and ictal epileptiform discharges; and presurgical ...
Christoph, Baumgartner, Susanne, Pirker
openaire +2 more sources
WS1.9. Advances in EEG Analysis – Wide-Band EEG, Dense-Array EEG and Quantitative EEG
Clinical Neurophysiology, 20211) Wide-band EEG As the faster frequency extreme of wide-band EEG, high frequency oscillation (HFO) was originally described by means of 1) micro-electrode recording in animal model, and ictal HFO occurred before clinical onset. Recently, 2 more types of HFO were well described; 2) invasive electrodes recording in epilepsy surgery (macro-invasive ...
openaire +1 more source
IEEE Transactions on Biomedical Engineering, 2020
Objective: Artifact subspace reconstruction (ASR) is an automatic, online-capable, component-based method that can effectively remove transient or large-amplitude artifacts contaminating electroencephalographic (EEG) data.
Chi-Yuan Chang +3 more
semanticscholar +1 more source
Objective: Artifact subspace reconstruction (ASR) is an automatic, online-capable, component-based method that can effectively remove transient or large-amplitude artifacts contaminating electroencephalographic (EEG) data.
Chi-Yuan Chang +3 more
semanticscholar +1 more source
Neurophysiologie Clinique/Clinical Neurophysiology, 2015
Long-term EEG in adults includes three modalities: sleep deprived-EEG lasting 1 to 3 hours, 24 hours ambulatory-EEG and continuous prolonged video-EEG lasting from several hours to several days. The main indications of long-term EEG are: syndromic classification of epilepsy; search for interictal discharges when epilepsy is suspected or for the purpose
V, Michel +3 more
openaire +2 more sources
Long-term EEG in adults includes three modalities: sleep deprived-EEG lasting 1 to 3 hours, 24 hours ambulatory-EEG and continuous prolonged video-EEG lasting from several hours to several days. The main indications of long-term EEG are: syndromic classification of epilepsy; search for interictal discharges when epilepsy is suspected or for the purpose
V, Michel +3 more
openaire +2 more sources
Emotions Recognition Using EEG Signals: A Survey
IEEE Transactions on Affective Computing, 2019Emotions have an important role in daily life, not only in human interaction, but also in decision-making processes, and in the perception of the world around us.
Soraia M. Alarcão, M. J. Fonseca
semanticscholar +1 more source
EEG frequency PCA in EEG‐ERP dynamics
Psychophysiology, 2017AbstractPrincipal components analysis (PCA) has long been used to decompose the ERP into components, and these mathematical entities are increasingly accepted as meaningful and useful representatives of the electrophysiological components constituting the ERP.
Barry, R. J. +1 more
openaire +3 more sources
CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding
International Conference on Learning RepresentationsElectroencephalography (EEG) is a non-invasive technique to measure and record brain electrical activity, widely used in various BCI and healthcare applications.
Jiquan Wang +7 more
semanticscholar +1 more source
A Bi-Hemisphere Domain Adversarial Neural Network Model for EEG Emotion Recognition
IEEE Transactions on Affective Computing, 2021In this paper, we propose a novel neural network model, called bi-hemisphere domain adversarial neural network (BiDANN) model, for electroencephalograph (EEG) emotion recognition.
Yang Li +5 more
semanticscholar +1 more source
A Review on Machine Learning for EEG Signal Processing in Bioengineering
IEEE Reviews in Biomedical Engineering, 2020Electroencephalography (EEG) has been a staple method for identifying certain health conditions in patients since its discovery. Due to the many different types of classifiers available to use, the analysis methods are also equally numerous.
M. Hosseini, Amin Hosseini, Kiarash Ahi
semanticscholar +1 more source
Electroencephalography and Clinical Neurophysiology, 1985
The neurophysiologic evaluation of patients with possible or proven paroxysmal disorders is no longer limited to routine laboratory EEGs or intensive inpatient monitoring. Expanded temporal sampling of the EEG, which increases the probability of documenting, characterizing, and quantitating the electrographic manifestations of these illnesses, is now ...
openaire +2 more sources
The neurophysiologic evaluation of patients with possible or proven paroxysmal disorders is no longer limited to routine laboratory EEGs or intensive inpatient monitoring. Expanded temporal sampling of the EEG, which increases the probability of documenting, characterizing, and quantitating the electrographic manifestations of these illnesses, is now ...
openaire +2 more sources