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Asbjørn Mohr Drewes

Inverse Modeling on Decomposed Electroencephalographic Data: A Way Forward?

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Standard

Inverse Modeling on Decomposed Electroencephalographic Data: A Way Forward? / Lelic, Dina; Gratkowski, Maciej; Valeriani, Massimiliano; Arendt-Nielsen, Lars; Mohr Drewes, Asbjørn.

I: Journal of Clinical Neurophysiology, Bind 26, Nr. 4, 2009, s. 227-235.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Harvard

Lelic, D, Gratkowski, M, Valeriani, M, Arendt-Nielsen, L & Mohr Drewes, A 2009, 'Inverse Modeling on Decomposed Electroencephalographic Data: A Way Forward?', Journal of Clinical Neurophysiology, bind 26, nr. 4, s. 227-235. https://doi.org/10.1097/WNP.0b013e3181aed1a1

APA

Lelic, D., Gratkowski, M., Valeriani, M., Arendt-Nielsen, L., & Mohr Drewes, A. (2009). Inverse Modeling on Decomposed Electroencephalographic Data: A Way Forward? Journal of Clinical Neurophysiology, 26(4), 227-235. https://doi.org/10.1097/WNP.0b013e3181aed1a1

CBE

Lelic D, Gratkowski M, Valeriani M, Arendt-Nielsen L, Mohr Drewes A. 2009. Inverse Modeling on Decomposed Electroencephalographic Data: A Way Forward?. Journal of Clinical Neurophysiology. 26(4):227-235. https://doi.org/10.1097/WNP.0b013e3181aed1a1

MLA

Vancouver

Lelic D, Gratkowski M, Valeriani M, Arendt-Nielsen L, Mohr Drewes A. Inverse Modeling on Decomposed Electroencephalographic Data: A Way Forward? Journal of Clinical Neurophysiology. 2009;26(4):227-235. https://doi.org/10.1097/WNP.0b013e3181aed1a1

Author

Lelic, Dina ; Gratkowski, Maciej ; Valeriani, Massimiliano ; Arendt-Nielsen, Lars ; Mohr Drewes, Asbjørn. / Inverse Modeling on Decomposed Electroencephalographic Data: A Way Forward?. I: Journal of Clinical Neurophysiology. 2009 ; Bind 26, Nr. 4. s. 227-235.

Bibtex

@article{211b9f00710f11deb2cc000ea68e967b,
title = "Inverse Modeling on Decomposed Electroencephalographic Data: A Way Forward?",
abstract = "Inverse modeling is typically applied to instantaneous electroencephalogram signals. However, this approach has several shortcomings including its instability to model multiple and deep located dipole sources and the interference of background noise may hamper the sensitivity, stability, and precision of the estimated dipoles. This article validates different dipole estimation techniques to find the most optimal combination of different analysis principles using both simulations and recordings. Electroencephalogram data were simulated with six known source locations. First, a dataset was simulated with sources chosen to mimic somatosensory-evoked potentials to electrical stimuli. Additionally, 20 further datasets were simulated each containing six randomly located and oriented sources. The simulated sources included superficial, deep, and simultaneously active sources. Furthermore, somatosensory-evoked potentials to median nerve stimuli were recorded from one subject. On both simulated and recorded evoked potential data, three different methods of signal decomposition were compared: independent component analysis (ICA), second-order blind identification (SOBI), and multichannel matching pursuit (MMP). For inverse modeling of the brain sources, the DIPFIT function of the EEGLAB software was used on raw and decomposed data. MMP was able to separate all simulated components that corresponded to superficial, deep, and simultaneously active sources. ICA and SOBI were only able to find components that corresponded to superficial sources. For the 20 randomized simulations, the results from the evoked potential simulation were reproduced. Inverse modeling on MMP components (atoms) was better than on ICA or SOBI components (P < 0.001). DIPFIT on MMP atoms localized 99.2% of the simulated dipoles in correct areas with their correct time/frequency distribution. DIPFIT on ICA and SOBI components localized 35% and 39.6%, respectively of the simulated dipoles in correct areas. As for the real-evoked potentials recorded in one subject, DIPFIT on MMP atoms allowed us to build a dipole model closer to the current physiological knowledge than dipole modeling of ICA and SOBI components. The results show that using MMP before inverse modeling is a reliable way to noninvasively estimate cortical activation.",
author = "Dina Lelic and Maciej Gratkowski and Massimiliano Valeriani and Lars Arendt-Nielsen and {Mohr Drewes}, Asbj{\o}rn",
year = "2009",
doi = "10.1097/WNP.0b013e3181aed1a1",
language = "English",
volume = "26",
pages = "227--235",
journal = "Journal of Clinical Neurophysiology",
issn = "0736-0258",
publisher = "LIPPINCOTT WILLIAMS & WILKINS",
number = "4",

}

RIS

TY - JOUR

T1 - Inverse Modeling on Decomposed Electroencephalographic Data: A Way Forward?

AU - Lelic, Dina

AU - Gratkowski, Maciej

AU - Valeriani, Massimiliano

AU - Arendt-Nielsen, Lars

AU - Mohr Drewes, Asbjørn

PY - 2009

Y1 - 2009

N2 - Inverse modeling is typically applied to instantaneous electroencephalogram signals. However, this approach has several shortcomings including its instability to model multiple and deep located dipole sources and the interference of background noise may hamper the sensitivity, stability, and precision of the estimated dipoles. This article validates different dipole estimation techniques to find the most optimal combination of different analysis principles using both simulations and recordings. Electroencephalogram data were simulated with six known source locations. First, a dataset was simulated with sources chosen to mimic somatosensory-evoked potentials to electrical stimuli. Additionally, 20 further datasets were simulated each containing six randomly located and oriented sources. The simulated sources included superficial, deep, and simultaneously active sources. Furthermore, somatosensory-evoked potentials to median nerve stimuli were recorded from one subject. On both simulated and recorded evoked potential data, three different methods of signal decomposition were compared: independent component analysis (ICA), second-order blind identification (SOBI), and multichannel matching pursuit (MMP). For inverse modeling of the brain sources, the DIPFIT function of the EEGLAB software was used on raw and decomposed data. MMP was able to separate all simulated components that corresponded to superficial, deep, and simultaneously active sources. ICA and SOBI were only able to find components that corresponded to superficial sources. For the 20 randomized simulations, the results from the evoked potential simulation were reproduced. Inverse modeling on MMP components (atoms) was better than on ICA or SOBI components (P < 0.001). DIPFIT on MMP atoms localized 99.2% of the simulated dipoles in correct areas with their correct time/frequency distribution. DIPFIT on ICA and SOBI components localized 35% and 39.6%, respectively of the simulated dipoles in correct areas. As for the real-evoked potentials recorded in one subject, DIPFIT on MMP atoms allowed us to build a dipole model closer to the current physiological knowledge than dipole modeling of ICA and SOBI components. The results show that using MMP before inverse modeling is a reliable way to noninvasively estimate cortical activation.

AB - Inverse modeling is typically applied to instantaneous electroencephalogram signals. However, this approach has several shortcomings including its instability to model multiple and deep located dipole sources and the interference of background noise may hamper the sensitivity, stability, and precision of the estimated dipoles. This article validates different dipole estimation techniques to find the most optimal combination of different analysis principles using both simulations and recordings. Electroencephalogram data were simulated with six known source locations. First, a dataset was simulated with sources chosen to mimic somatosensory-evoked potentials to electrical stimuli. Additionally, 20 further datasets were simulated each containing six randomly located and oriented sources. The simulated sources included superficial, deep, and simultaneously active sources. Furthermore, somatosensory-evoked potentials to median nerve stimuli were recorded from one subject. On both simulated and recorded evoked potential data, three different methods of signal decomposition were compared: independent component analysis (ICA), second-order blind identification (SOBI), and multichannel matching pursuit (MMP). For inverse modeling of the brain sources, the DIPFIT function of the EEGLAB software was used on raw and decomposed data. MMP was able to separate all simulated components that corresponded to superficial, deep, and simultaneously active sources. ICA and SOBI were only able to find components that corresponded to superficial sources. For the 20 randomized simulations, the results from the evoked potential simulation were reproduced. Inverse modeling on MMP components (atoms) was better than on ICA or SOBI components (P < 0.001). DIPFIT on MMP atoms localized 99.2% of the simulated dipoles in correct areas with their correct time/frequency distribution. DIPFIT on ICA and SOBI components localized 35% and 39.6%, respectively of the simulated dipoles in correct areas. As for the real-evoked potentials recorded in one subject, DIPFIT on MMP atoms allowed us to build a dipole model closer to the current physiological knowledge than dipole modeling of ICA and SOBI components. The results show that using MMP before inverse modeling is a reliable way to noninvasively estimate cortical activation.

U2 - 10.1097/WNP.0b013e3181aed1a1

DO - 10.1097/WNP.0b013e3181aed1a1

M3 - Journal article

C2 - 19584750

VL - 26

SP - 227

EP - 235

JO - Journal of Clinical Neurophysiology

JF - Journal of Clinical Neurophysiology

SN - 0736-0258

IS - 4

ER -