Unconvering neural independent components from highly artifactual TMS-evoked EEG data

Julio C. Hernandez-Pavon, , Johanna Metsomaa , Tuomas Mutanen , Matti Stenroos, Hanna Mäki, Risto Ilmoniemi, Jukka Sarvas

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a powerful tool for studying cortical excitability and connectivity. To enhance the EEG interpretation, independent component analysis (ICA) has been used to separate the data into independent components (ICs). However, TMS can evoke large artifacts in EEG, which may greatly distort the ICA separation. The removal of such artifactual EEG from the data is a difficult task. In this paper we study how badly the large artifacts distort the ICA separation, and whether the distortions could be avoided without removing the artifacts. We first show that, in the ICA separation, the time courses of the ICs are not affected by the large artifacts, but their topographies could be greatly distorted. Next, we show how this distortion can be circumvented. We introduce a novel technique of suppression, by which the EEG data are modified so that the ICA separation of the suppressed data becomes reliable. The suppression, instead of removing the artifactual EEG, rescales all the data to about the same magnitude as the neural EEG. For the suppressed data, ICA returns the original time courses, but instead of the original topographies, it returns modified ones, which can be used, e.g., for the source localization. We present three suppression methods based on principal component analysis, wavelet analysis, and whitening of the data matrix, respectively. We test the methods with numerical simulations. The results show that the suppression improves the source localization.
Original languageEnglish
JournalJournal of Neuroscience Methods
Volume209
Issue number1
Pages (from-to)144-157
Number of pages14
ISSN0165-0270
DOIs
Publication statusPublished - 2012
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 217 Medical engineering
  • Transcranial magentic stimulation
  • Electroencephalography
  • Independent component analysis
  • Principal component analysis
  • Wavelets
  • Dipole source localization
  • Broca's area

Cite this

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title = "Unconvering neural independent components from highly artifactual TMS-evoked EEG data",
abstract = "Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a powerful tool for studying cortical excitability and connectivity. To enhance the EEG interpretation, independent component analysis (ICA) has been used to separate the data into independent components (ICs). However, TMS can evoke large artifacts in EEG, which may greatly distort the ICA separation. The removal of such artifactual EEG from the data is a difficult task. In this paper we study how badly the large artifacts distort the ICA separation, and whether the distortions could be avoided without removing the artifacts. We first show that, in the ICA separation, the time courses of the ICs are not affected by the large artifacts, but their topographies could be greatly distorted. Next, we show how this distortion can be circumvented. We introduce a novel technique of suppression, by which the EEG data are modified so that the ICA separation of the suppressed data becomes reliable. The suppression, instead of removing the artifactual EEG, rescales all the data to about the same magnitude as the neural EEG. For the suppressed data, ICA returns the original time courses, but instead of the original topographies, it returns modified ones, which can be used, e.g., for the source localization. We present three suppression methods based on principal component analysis, wavelet analysis, and whitening of the data matrix, respectively. We test the methods with numerical simulations. The results show that the suppression improves the source localization.",
keywords = "217 Medical engineering, Transcranial magentic stimulation, Electroencephalography, Independent component analysis, Principal component analysis, Wavelets, Dipole source localization, Broca's area",
author = "Hernandez-Pavon,, {Julio C.} and Johanna Metsomaa and Tuomas Mutanen and Matti Stenroos and Hanna M{\"a}ki and Risto Ilmoniemi and Jukka Sarvas",
year = "2012",
doi = "10.1016/j.neumeth.2012.05.029",
language = "English",
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pages = "144--157",
journal = "Journal of Neuroscience Methods",
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Unconvering neural independent components from highly artifactual TMS-evoked EEG data. / Hernandez-Pavon, , Julio C. ; Metsomaa , Johanna ; Mutanen , Tuomas; Stenroos, Matti; Mäki, Hanna ; Ilmoniemi, Risto; Sarvas, Jukka.

In: Journal of Neuroscience Methods, Vol. 209, No. 1, 2012, p. 144-157.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Unconvering neural independent components from highly artifactual TMS-evoked EEG data

AU - Hernandez-Pavon, , Julio C.

AU - Metsomaa , Johanna

AU - Mutanen , Tuomas

AU - Stenroos, Matti

AU - Mäki, Hanna

AU - Ilmoniemi, Risto

AU - Sarvas, Jukka

PY - 2012

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N2 - Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a powerful tool for studying cortical excitability and connectivity. To enhance the EEG interpretation, independent component analysis (ICA) has been used to separate the data into independent components (ICs). However, TMS can evoke large artifacts in EEG, which may greatly distort the ICA separation. The removal of such artifactual EEG from the data is a difficult task. In this paper we study how badly the large artifacts distort the ICA separation, and whether the distortions could be avoided without removing the artifacts. We first show that, in the ICA separation, the time courses of the ICs are not affected by the large artifacts, but their topographies could be greatly distorted. Next, we show how this distortion can be circumvented. We introduce a novel technique of suppression, by which the EEG data are modified so that the ICA separation of the suppressed data becomes reliable. The suppression, instead of removing the artifactual EEG, rescales all the data to about the same magnitude as the neural EEG. For the suppressed data, ICA returns the original time courses, but instead of the original topographies, it returns modified ones, which can be used, e.g., for the source localization. We present three suppression methods based on principal component analysis, wavelet analysis, and whitening of the data matrix, respectively. We test the methods with numerical simulations. The results show that the suppression improves the source localization.

AB - Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a powerful tool for studying cortical excitability and connectivity. To enhance the EEG interpretation, independent component analysis (ICA) has been used to separate the data into independent components (ICs). However, TMS can evoke large artifacts in EEG, which may greatly distort the ICA separation. The removal of such artifactual EEG from the data is a difficult task. In this paper we study how badly the large artifacts distort the ICA separation, and whether the distortions could be avoided without removing the artifacts. We first show that, in the ICA separation, the time courses of the ICs are not affected by the large artifacts, but their topographies could be greatly distorted. Next, we show how this distortion can be circumvented. We introduce a novel technique of suppression, by which the EEG data are modified so that the ICA separation of the suppressed data becomes reliable. The suppression, instead of removing the artifactual EEG, rescales all the data to about the same magnitude as the neural EEG. For the suppressed data, ICA returns the original time courses, but instead of the original topographies, it returns modified ones, which can be used, e.g., for the source localization. We present three suppression methods based on principal component analysis, wavelet analysis, and whitening of the data matrix, respectively. We test the methods with numerical simulations. The results show that the suppression improves the source localization.

KW - 217 Medical engineering

KW - Transcranial magentic stimulation

KW - Electroencephalography

KW - Independent component analysis

KW - Principal component analysis

KW - Wavelets

KW - Dipole source localization

KW - Broca's area

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DO - 10.1016/j.neumeth.2012.05.029

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EP - 157

JO - Journal of Neuroscience Methods

JF - Journal of Neuroscience Methods

SN - 0165-0270

IS - 1

ER -