Concept

Neurofeedback

Related publications (293)

Breathing is coupled with voluntary initiation of mental imagery

Olaf Blanke, Oliver Alan Kannape, Hyeongdong Park

Previous research has suggested that bodily signals from internal organs are associated with diverse cortical and subcortical processes involved in sensory-motor functions, beyond homeostatic reflexes. For instance, a recent study demonstrated that the pre ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2022

Identifying uncertainty states during wayfinding in indoor environments: An EEG classification Study

Mahsa Shoaran, Bingzhao Zhu

The researchers used a machine-learning classification approach to better understand neurological features associated with periods of wayfinding uncertainty. The participants (n = 30) were asked to complete wayfinding tasks of varying difficulty in a virtu ...

ELSEVIER SCI LTD2022

Predictors of real-time fMRI neurofeedback performance and improvement-A machine learning mega-analysis

Dimitri Nestor Alice Van De Ville, Frank Scharnowski, Yury Koush, Kirsten Emmert

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinic ...

2021

Electrophysiological correlates of visual backward masking in patients with bipolar disorder

Michael Herzog, Simona Adele Garobbio, Maya Roinishvili, Ophélie Gladys Favrod

In visual backward masking (VBM), a target is followed by a mask that decreases target discriminability. Schizophrenia patients (SZ) show strong and reproducible masking impairments, which are associated with reduced EEG amplitudes. Patients with bipolar d ...

2021

Automatic detection of generalized paroxysmal fast activity in interictal EEG using time-frequency analysis

Amir Hossein Omidvarnia

Objective: Markup of generalized interictal epileptiform discharges (IEDs) on EEG is an important step in the diagnosis and characterization of epilepsy. However, manual EEG markup is a time-consuming, subjective, and the specialized task where the human r ...

PERGAMON-ELSEVIER SCIENCE LTD2021

Effective connectivity in brain networks estimated using EEG signals is altered in children with ADHD

Amir Hossein Omidvarnia

This study presents a methodology developed for estimating effective connectivity in brain networks (BNs) using multichannel scalp EEG recordings. The methodology uses transfer entropy as an information transfer measure to detect pair-wise directed informa ...

PERGAMON-ELSEVIER SCIENCE LTD2021

An EEG-based methodology for the estimation of functional brain connectivity networks: Application to the analysis of newborn EEG seizure

Amir Hossein Omidvarnia

This study presents a new methodology for obtaining functional brain networks (FBNs) using multichannel scalp EEG recordings. The developed methodology extracts pair-wise phase synchrony between EEG electrodes to obtain FBNs at delta, theta, and alpha-band ...

ELSEVIER SCI LTD2021

Connectivity Measures Differentiate Cortical and Subcortical Sub-Acute Ischemic Stroke Patients

Silvestro Micera, Michael Lassi

Brain lesions caused by cerebral ischemia lead to network disturbances in both hemispheres, causing a subsequent reorganization of functional connectivity both locally and remotely with respect to the injury. Quantitative electroencephalography (qEEG) meth ...

FRONTIERS MEDIA SA2021

The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T

Rolf Gruetter, João Pedro Forjaco Jorge, Arwen Blanche Giraud, François Lazeyras, Giannina Rita Iannotti

Both electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are non-invasive methods that show complementary aspects of human brain activity. Despite measuring different proxies of brain activity, both the measured blood-oxygenation ...

2021

Interpreting deep learning models for epileptic seizure detection on EEG signals

David Atienza Alonso, Marina Zapater Sancho, Tomas Teijeiro Campo, Valentin Alexandre Guy Gabeff, Leila Cammoun

While Deep Learning (DL) is often considered the state-of-the art for Artificial Intelligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability of neur ...

2021

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