Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study

Show full item record



Permalink

http://hdl.handle.net/10138/166451

Citation

Haumann , N T , Parkkonen , L , Kliuchko , M , Vuust , P & Brattico , E 2016 , ' Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study ' , Computational Intelligence and Neuroscience . https://doi.org/10.1155/2016/7489108

Title: Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study
Author: Haumann, Niels Trusbak; Parkkonen, Lauri; Kliuchko, Marina; Vuust, Peter; Brattico, Elvira
Contributor: University of Helsinki, Behavioural Sciences
University of Helsinki, Aarhus University
Date: 2016
Language: eng
Number of pages: 10
Belongs to series: Computational Intelligence and Neuroscience
ISSN: 1687-5265
URI: http://hdl.handle.net/10138/166451
Abstract: We here compared results achieved by applying popular methods for reducing artifacts in magnetoencephalography (MEG) and electroencephalography (EEG) recordings of the auditory evoked Mismatch Negativity (MMN) responses in healthy adult subjects. We compared the Signal Space Separation (SSS) and temporal SSS (tSSS) methods for reducing noise from external and nearby sources. Our results showed that tSSS reduces the interference level more reliably than plain SSS, particularly for MEG gradiometers, also for healthy subjects not wearing strongly interfering magnetic material. Therefore, tSSS is recommended over SSS. Furthermore, we found that better artifact correction is achieved by applying Independent Component Analysis (ICA) in comparison to Signal Space Projection (SSP). Although SSP reduces the baseline noise level more than ICA, SSP also significantly reduces the signal-slightly more than it reduces the artifacts interfering with the signal. However, ICA also adds noise, or correction errors, to the wave form when the signal-to-noise ratio (SNR) in the original data is relatively low-in particular to EEG and to MEG magnetometer data. In conclusion, ICA is recommended over SSP, but one should be careful when applying ICA to reduce artifacts on neurophysiological data with relatively low SNR.
Subject: INDEPENDENT COMPONENT ANALYSIS
SIGNAL-SPACE SEPARATION
BLIND SOURCE SEPARATION
EEG DATA
OCULAR ARTIFACTS
MISMATCH NEGATIVITY
MUSCLE ARTIFACTS
MIXING MATRIX
MEG
REMOVAL
3112 Neurosciences
6162 Cognitive science
Rights:


Files in this item

Total number of downloads: Loading...

Files Size Format View
7489108.pdf 3.251Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record