Lorenz Esch

Real-Time Estimation and Visualization of Functional Connectivity in the Human Brain


Front	Back

ISBN:

978-3-8440-7773-5

Series:

Medizintechnik

Keywords:

Neuroscience; Real-Time Data Processing; Functional Connectivity; Source Localization; MEG; EEG; Software; Electrophysiology

Type of publication:

Thesis

Language:

English

Pages:

148 pages

Figures:

53 figures

Weight:

219 g

Format:

21 x 14,8 cm

Bindung:

Paperback

Price:

45,80 € / 57,30 SFr

Published:

December 2020

Buy:

DOI:

10.2370/9783844077735 (Online document)

Download:

Available PDF-Files for this title:

You need the Adobe Reader, to open the files. Here you get help and information, for the download.

These files are not printable.


	Document		Document
	Type		PDF
	Costs		11,45 EUR
	Action		Purchase in obligation and display of file - 4,7 MB (4912918 Byte)
	Action		Purchase in obligation and download of file - 4,7 MB (4912918 Byte)


	Document		Table of contents
	Type		PDF
	Costs		free
	Action		Diplay of file - 476 kB (487320 Byte)
	Action		Download of file - 476 kB (487320 Byte)

User settings for registered users

You can change your address here or download your paid documents again.

User:	Not logged in.
Actions:	Login / Register Forgotten your password?

Recommendation:

You want to recommend this title?

Review copy

Here you can order a review copy.

Link

You want to link this page? Click here.

Abstract

Real-time processing of neural data provides new opportunities for neuroscience research. This approach yields more intuitive insights into instantaneous brain functions and creates the foundation for a wide range of neurofeedback scenarios. Real-time processing also allows early assessment of data quality and validity of the experiment setup. This enables early identification of possible problems and thus helps to optimize the measurement procedure. Magnetoencephalography (MEG) and Electroencephalography (EEG) are non-invasive electrophysiological methods with a high temporal resolution. Tools and software toolboxes have already been proposed that monitor and process M/EEG data in real-time.

This work introduces new tools to acquire and process electrophysiological data streams in real-time. A special emphasis is put on the estimation and visualization of functional connectivity networks. Functional connectivity estimation has been hardly ever considered in the real-time setting so far. The methods described in this work allow the real-time estimation and visualization of large functional connectivity networks based on sensor- and source-level M/EEG data. The new tools were integrated into the open-source MNE-CPP software, which provides an API and standalone GUI applications. The MNE-CPP project caters both software developers and users with little or no coding experience.