In this paper, we analyze the impact of the two main factors that intervene in EEG source connectivity processing: i) the algorithm used to solve the EEG ...
In this paper, we report a qualitative and quantitative comparison of methods aimed at identifying epileptogenic cortical networks from scalp EEG data (see fig.
Identification of epileptogenic networks from dense EEG: A model ...
www.researchgate.net › ... › EEG
Epilepsy is a network disease. Identifying the epileptogenic networks from noninvasive recordings is a challenging issue. In this context, M/EEG source ...
Identification of Interictal Epileptic Networks from Dense-EEG - PubMed
pubmed.ncbi.nlm.nih.gov › ...
In this paper, we analyze the effect of the two key factors involved in EEG source connectivity processing: (i) the algorithm used in the solution of the EEG ...
Missing: study. | Show results with:study.
In this paper, we analyze the effect of the two key factors involved in EEG source connectivity processing: i) the algorithm used in the solution of the EEG ...
People also ask
What is an EEG for diagnosing epilepsy?
What are EEG waves for epilepsy?
What are the patterns of epileptiform EEG?
How do you classify EEG data?
Jan 25, 2023 · A digital workflow for mapping individual epileptogenic zone networks based on patient magnetic resonance imaging and stereoelectroencephalography recordings.
Oct 18, 2023 · In this paper, we propose an EEG-based improved automatic seizure detection system using a Deep neural network (DNN) and Binary dragonfly algorithm (BDFA).
This chapter describes the state of the art of computational EEG analysis. It reviews and discusses different computational approaches and achievements in the ...
Dec 15, 2023 · Here we proposed a two-stream model with unsupervised learning and graph convolutional network tailored to the SEEG and CCEP datasets in individual patients.
In this study, we proposed a dynamic method using a deep learning model (Epileptic-Net) to detect an epileptic seizure.