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

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Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study. / Haumann, Niels Trusbak; Parkkonen, Lauri; Kliuchko, Marina; Vuust, Peter; Brattico, Elvira.

In: Computational Intelligence and Neuroscience, Vol. 2016, 07.2016, p. 1-10.

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Haumann, Niels Trusbak ; Parkkonen, Lauri ; Kliuchko, Marina ; Vuust, Peter ; Brattico, Elvira. / Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study. In: Computational Intelligence and Neuroscience. 2016 ; Vol. 2016. pp. 1-10.

Bibtex

@article{c9cbcd9dd0c44b07842744a4865ab77e,
title = "Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study",
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 waveform 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.",
keywords = "SSS, tSSS, SSP, ICA, artifacts, EEG, MEG, MMN",
author = "Haumann, {Niels Trusbak} and Lauri Parkkonen and Marina Kliuchko and Peter Vuust and Elvira Brattico",
year = "2016",
month = jul,
doi = "10.1155/2016/7489108",
language = "English",
volume = "2016",
pages = "1--10",
journal = "Computational Intelligence and Neuroscience",
issn = "1687-5265",
publisher = "Hindawi Publishing Corporation",

}

RIS

TY - JOUR

T1 - Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study

AU - Haumann, Niels Trusbak

AU - Parkkonen, Lauri

AU - Kliuchko, Marina

AU - Vuust, Peter

AU - Brattico, Elvira

PY - 2016/7

Y1 - 2016/7

N2 - 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 waveform 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.

AB - 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 waveform 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.

KW - SSS

KW - tSSS

KW - SSP

KW - ICA

KW - artifacts

KW - EEG

KW - MEG

KW - MMN

U2 - 10.1155/2016/7489108

DO - 10.1155/2016/7489108

M3 - Journal article

C2 - 27524998

VL - 2016

SP - 1

EP - 10

JO - Computational Intelligence and Neuroscience

JF - Computational Intelligence and Neuroscience

SN - 1687-5265

ER -