Edge Sparse Basis Network: An Deep Learning Framework for EEG Source Localization

02/18/2021
by   Chen Wei, et al.
43

EEG source localization is an important technical issue in EEG analysis. Despite many numerical methods existed for EEG source localization, they all rely on strong priors and the deep sources are intractable. Here we propose a deep learning framework using spatial basis function decomposition for EEG source localization. This framework combines the edge sparsity prior and Gaussian source basis, called Edge Sparse Basis Network (ESBN). The performance of ESBN is validated by both synthetic data and real EEG data during motor tasks. The results suggest that the supervised ESBN outperforms the traditional numerical methods in synthetic data and the unsupervised fine-tuning provides more focal and accurate localizations in real data. Our proposed deep learning framework can be extended to account for other source priors, and the real-time property of ESBN can facilitate the applications of EEG in brain-computer interfaces and clinics.

READ FULL TEXT

page 6

page 7

12/02/2021

Embedding Decomposition for Artifacts Removal in EEG Signals

Electroencephalogram (EEG) recordings are often contaminated with artifa...
08/25/2020

Improving EEG Source Localization through Spatio-temporal Sparse Bayesian Learning

Sparse Bayesian Learning (SBL) approaches to the EEG inverse problem suc...
06/05/2019

Probabilistic Structure Learning for EEG/MEG Source Imaging with Hierarchical Graph Prior

Brain source imaging is an important method for noninvasively characteri...
01/08/2020

EEG-based Drowsiness Estimation for Driving Safety using Deep Q-Learning

Fatigue is the most vital factor of road fatalities and one manifestatio...
04/08/2019

Implementation of a Daemon for OpenBCI

This document describes a technical study of the electroencephalographic...
09/26/2020

Cross-individual Recognition of Emotions by a Dynamic Entropy based on Pattern Learning with EEG features

Use of the electroencephalogram (EEG) and machine learning approaches to...
01/31/2020

Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the help of Bayesian Uncertainty Modelling

The electroencephalography (EEG) source imaging problem is very sensitiv...