Unmixing oscillatory brain activity by EEG source localization and empirical mode decomposition

Sofie Therese Hansen*, Apit Hemakom, Mads Gylling Safeldt, Lærke Karen Krohne, Kristoffer Hougaard Madsen, Hartwig R. Siebner, Danilo P. Mandic, Lars Kai Hansen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Neuronal activity is composed of synchronous and asynchronous oscillatory activity at different frequencies. The neuronal oscillations occur at time scales well matched to the temporal resolution of electroencephalography (EEG); however, to derive meaning from the electrical brain activity as measured from the scalp, it is useful to decompose the EEG signal in space and time. In this study, we elaborate on the investigations into source-based signal decomposition of EEG. Using source localization, the electrical brain signal is spatially unmixed and the neuronal dynamics from a region of interest are analyzed using empirical mode decomposition (EMD), a technique aimed at detecting periodic signals. We demonstrate, first in simulations, that the EMD is more accurate when applied to the spatially unmixed signal compared to the scalp-level signal. Furthermore, on EEG data recorded simultaneously with transcranial magnetic stimulation (TMS) over the hand area of the primary motor cortex, we observe a link between the peak to peak amplitude of the motor-evoked potential (MEP) and the phase of the decomposed localized electrical activity before TMS onset. The results thus encourage combination of source localization and EMD in the pursuit of further insight into the mechanisms of the brain with respect to the phase and frequency of the electrical oscillations and their cortical origin.

Original languageEnglish
Article number5618303
JournalComputational Intelligence and Neuroscience
Volume2019
Number of pages16
ISSN1687-5265
DOIs
Publication statusPublished - 1 Jan 2019

Cite this

@article{8afcdfeb829143869dd205591a2e2232,
title = "Unmixing oscillatory brain activity by EEG source localization and empirical mode decomposition",
abstract = "Neuronal activity is composed of synchronous and asynchronous oscillatory activity at different frequencies. The neuronal oscillations occur at time scales well matched to the temporal resolution of electroencephalography (EEG); however, to derive meaning from the electrical brain activity as measured from the scalp, it is useful to decompose the EEG signal in space and time. In this study, we elaborate on the investigations into source-based signal decomposition of EEG. Using source localization, the electrical brain signal is spatially unmixed and the neuronal dynamics from a region of interest are analyzed using empirical mode decomposition (EMD), a technique aimed at detecting periodic signals. We demonstrate, first in simulations, that the EMD is more accurate when applied to the spatially unmixed signal compared to the scalp-level signal. Furthermore, on EEG data recorded simultaneously with transcranial magnetic stimulation (TMS) over the hand area of the primary motor cortex, we observe a link between the peak to peak amplitude of the motor-evoked potential (MEP) and the phase of the decomposed localized electrical activity before TMS onset. The results thus encourage combination of source localization and EMD in the pursuit of further insight into the mechanisms of the brain with respect to the phase and frequency of the electrical oscillations and their cortical origin.",
author = "Hansen, {Sofie Therese} and Apit Hemakom and {Gylling Safeldt}, Mads and Krohne, {L{\ae}rke Karen} and Madsen, {Kristoffer Hougaard} and Siebner, {Hartwig R.} and Mandic, {Danilo P.} and Hansen, {Lars Kai}",
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language = "English",
volume = "2019",
journal = "Computational Intelligence and Neuroscience",
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Unmixing oscillatory brain activity by EEG source localization and empirical mode decomposition. / Hansen, Sofie Therese; Hemakom, Apit; Gylling Safeldt, Mads; Krohne, Lærke Karen; Madsen, Kristoffer Hougaard; Siebner, Hartwig R.; Mandic, Danilo P.; Hansen, Lars Kai.

In: Computational Intelligence and Neuroscience, Vol. 2019, 5618303, 01.01.2019.

Research output: Contribution to journalJournal articleResearchpeer-review

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AU - Hansen, Sofie Therese

AU - Hemakom, Apit

AU - Gylling Safeldt, Mads

AU - Krohne, Lærke Karen

AU - Madsen, Kristoffer Hougaard

AU - Siebner, Hartwig R.

AU - Mandic, Danilo P.

AU - Hansen, Lars Kai

PY - 2019/1/1

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N2 - Neuronal activity is composed of synchronous and asynchronous oscillatory activity at different frequencies. The neuronal oscillations occur at time scales well matched to the temporal resolution of electroencephalography (EEG); however, to derive meaning from the electrical brain activity as measured from the scalp, it is useful to decompose the EEG signal in space and time. In this study, we elaborate on the investigations into source-based signal decomposition of EEG. Using source localization, the electrical brain signal is spatially unmixed and the neuronal dynamics from a region of interest are analyzed using empirical mode decomposition (EMD), a technique aimed at detecting periodic signals. We demonstrate, first in simulations, that the EMD is more accurate when applied to the spatially unmixed signal compared to the scalp-level signal. Furthermore, on EEG data recorded simultaneously with transcranial magnetic stimulation (TMS) over the hand area of the primary motor cortex, we observe a link between the peak to peak amplitude of the motor-evoked potential (MEP) and the phase of the decomposed localized electrical activity before TMS onset. The results thus encourage combination of source localization and EMD in the pursuit of further insight into the mechanisms of the brain with respect to the phase and frequency of the electrical oscillations and their cortical origin.

AB - Neuronal activity is composed of synchronous and asynchronous oscillatory activity at different frequencies. The neuronal oscillations occur at time scales well matched to the temporal resolution of electroencephalography (EEG); however, to derive meaning from the electrical brain activity as measured from the scalp, it is useful to decompose the EEG signal in space and time. In this study, we elaborate on the investigations into source-based signal decomposition of EEG. Using source localization, the electrical brain signal is spatially unmixed and the neuronal dynamics from a region of interest are analyzed using empirical mode decomposition (EMD), a technique aimed at detecting periodic signals. We demonstrate, first in simulations, that the EMD is more accurate when applied to the spatially unmixed signal compared to the scalp-level signal. Furthermore, on EEG data recorded simultaneously with transcranial magnetic stimulation (TMS) over the hand area of the primary motor cortex, we observe a link between the peak to peak amplitude of the motor-evoked potential (MEP) and the phase of the decomposed localized electrical activity before TMS onset. The results thus encourage combination of source localization and EMD in the pursuit of further insight into the mechanisms of the brain with respect to the phase and frequency of the electrical oscillations and their cortical origin.

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