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Mining event-related brain dynamics

Mining event-related brain dynamics
Mining event-related brain dynamics
This article provides a new, more comprehensive view of event-related brain dynamics founded on an information-based approach to modeling electroencephalographic (EEG) dynamics. Most EEG research focuses either on peaks 'evoked' in average event-related potentials (ERPs) or on changes 'induced' in the EEG power spectrum by experimental events. Although these measures are nearly complementary, they do not fully model the event-related dynamics in the data, and cannot isolate the signals of the contributing cortical areas. We propose that many ERPs and other EEG features are better viewed as time/frequency perturbations of underlying field potential processes. The new approach combines independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization that measures EEG source dynamics without requiring an explicit head model.
1364-6613
204-210
Makeig, Scott
13e376cb-709c-4377-ba95-9f55f312d9ec
Debener, Stefan
e6bf9143-09a8-45c0-8536-3564885375d4
Onton, Julie
1a3c21f2-7283-4303-a310-d475eeb5cad0
Delorme, Arnaud
9aa63e16-3a16-49c0-89b7-e08108d06c8e
Makeig, Scott
13e376cb-709c-4377-ba95-9f55f312d9ec
Debener, Stefan
e6bf9143-09a8-45c0-8536-3564885375d4
Onton, Julie
1a3c21f2-7283-4303-a310-d475eeb5cad0
Delorme, Arnaud
9aa63e16-3a16-49c0-89b7-e08108d06c8e

Makeig, Scott, Debener, Stefan, Onton, Julie and Delorme, Arnaud (2004) Mining event-related brain dynamics. Trends in Cognitive Sciences, 8 (5), 204-210. (doi:10.1016/j.tics.2004.03.008).

Record type: Article

Abstract

This article provides a new, more comprehensive view of event-related brain dynamics founded on an information-based approach to modeling electroencephalographic (EEG) dynamics. Most EEG research focuses either on peaks 'evoked' in average event-related potentials (ERPs) or on changes 'induced' in the EEG power spectrum by experimental events. Although these measures are nearly complementary, they do not fully model the event-related dynamics in the data, and cannot isolate the signals of the contributing cortical areas. We propose that many ERPs and other EEG features are better viewed as time/frequency perturbations of underlying field potential processes. The new approach combines independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization that measures EEG source dynamics without requiring an explicit head model.

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More information

Published date: May 2004

Identifiers

Local EPrints ID: 27646
URI: http://eprints.soton.ac.uk/id/eprint/27646
ISSN: 1364-6613
PURE UUID: 5e5bd7a8-13a0-4a27-9085-d4a588ee0af9

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Date deposited: 26 Apr 2006
Last modified: 15 Mar 2024 07:20

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Contributors

Author: Scott Makeig
Author: Stefan Debener
Author: Julie Onton
Author: Arnaud Delorme

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