Seizure suppression by adenosine-releasing cells is independent of seizure frequency

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Objective: Long-term monitoring of people with epilepsy based on electroencephalography (EEG) and intracranial EEG (iEEG) has the potential to deliver key clinical information for personalised epilepsy treatment. More specifically, in outpatient settings, ...

The epileptic brain is the result of a sequence of events transforming normal neuronal populations into hyperexcitable networks supporting recurrent seizure generation. These modifications are known to induce fundamental alterations of circuit function and ...

Transient electronics enabling devices to safely disappear in the environment can be applied not only in green electronics, but also in bioelectronic medicine. Neural implants able to degrade harmlessly inside the body eliminate the need for removal surger ...

The use of TMS-EEG coupling as a neuroimaging tool for the functional exploration of the human brain recently gained strong interest. If this tool directly inherits the fine temporal resolution from EEG, its spatial counterpart remains unknown. In this stu ...

Early and accurate detection of epileptic seizures is an extremely important therapeutic goal due to the severity of complications it can prevent. To this end, a low-power machine learning-based seizure detection implemented on an FPGA is proposed in this ...

Recent years have seen growing interest in leveraging deep learning models for monitoring epilepsy patients based on electroencephalographic (EEG) signals. However, these approaches often exhibit poor generalization when applied outside of the setting in w ...

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 ...

Epilepsy affects more than 40 million people worldwide, constituting one of the most debilitating disorders of the Central Nervous System (CNS). It results from an imbalance in the electrical activity of neurons, which is primarily mediated by calcium ions ...

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 ...

Seizure suppression by adenosine-releasing cells is independent of seizure frequency

Chattez avec Graph Search

Approximate Zero-Crossing: A new interpretable, highly discriminative and low-complexity feature for EEG and iEEG seizure detection

Disruption of layer-specific visual processing in a model of focal neocortical epilepsy

Development of a Transient Neural Interface for Minimally Invasive Recording and Stimulation

Exploring the spatial resolution of TMS-EEG coupling on the sensorimotor region

Hardware-Friendly Random Forest Classification of iEEG Signals for Implantable Seizure Detection

M2D2: Maximum-Mean-Discrepancy Decoder for Temporal Localization of Epileptic Brain Activities

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

Recent Developments on the Use of Nanomaterials for the Treatment of Epilepsy

Interpreting deep learning models for epileptic seizure detection on EEG signals