Estimating neural sources from each time-frequency component of magnetoencephalographic data

Kensuke Sekihara; Srikantan S. Nagarajan; David Poeppel; Satoru Miyauchi; Norio Fujimaki; Hideaki Koizumi; Yasushi Miyashita

doi:10.1109/10.841336

Estimating neural sources from each time-frequency component of magnetoencephalographic data

Kensuke Sekihara, Srikantan S. Nagarajan, David Poeppel, Satoru Miyauchi, Norio Fujimaki, Hideaki Koizumi, Yasushi Miyashita

Psychology

Research output: Contribution to journal › Article › peer-review

Abstract

We have developed a method that incorporates the time-frequency characteristics of neural sources into magnetoencephalographic (MEG) source estimation. This method, referred to as the time-frequency multiple-signal- classification algorithm, allows the locations of neural sources to be estimated from any time-frequency region of interest. In this paper, we formulate the method based on the most general form of the quadratic time- frequency representations. We then apply it to two kinds of nonstationary MEG data: gamma-band (frequency range between 30-100 Hz) auditory activity data and spontaneous MEG data. Our method successfully detected the gamma-band source slightly medial to the N1m source location. The method was able to selectively localize sources for alpha-rhythm bursts at different locations. It also detected the mu-rhythm source from the alpha-rhythm-dominant MEG data that was measured with the subject's eyes closed. The results of these applications validate the effectiveness of the time-frequency MUSIC algorithm for selectively localizing sources having different time-frequency signatures.

Original language	English (US)
Pages (from-to)	642-653
Number of pages	12
Journal	IEEE Transactions on Biomedical Engineering
Volume	47
Issue number	5
DOIs	https://doi.org/10.1109/10.841336
State	Published - 2000

Keywords

Biomagnetism
Biomedical signal processing
Functional brain imaging
Inverse problems
Time-frequency analysis

ASJC Scopus subject areas

Biomedical Engineering

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10.1109/10.841336

Cite this

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title = "Estimating neural sources from each time-frequency component of magnetoencephalographic data",

abstract = "We have developed a method that incorporates the time-frequency characteristics of neural sources into magnetoencephalographic (MEG) source estimation. This method, referred to as the time-frequency multiple-signal- classification algorithm, allows the locations of neural sources to be estimated from any time-frequency region of interest. In this paper, we formulate the method based on the most general form of the quadratic time- frequency representations. We then apply it to two kinds of nonstationary MEG data: gamma-band (frequency range between 30-100 Hz) auditory activity data and spontaneous MEG data. Our method successfully detected the gamma-band source slightly medial to the N1m source location. The method was able to selectively localize sources for alpha-rhythm bursts at different locations. It also detected the mu-rhythm source from the alpha-rhythm-dominant MEG data that was measured with the subject's eyes closed. The results of these applications validate the effectiveness of the time-frequency MUSIC algorithm for selectively localizing sources having different time-frequency signatures.",

keywords = "Biomagnetism, Biomedical signal processing, Functional brain imaging, Inverse problems, Time-frequency analysis",

author = "Kensuke Sekihara and Nagarajan, {Srikantan S.} and David Poeppel and Satoru Miyauchi and Norio Fujimaki and Hideaki Koizumi and Yasushi Miyashita",

year = "2000",

doi = "10.1109/10.841336",

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AU - Nagarajan, Srikantan S.

AU - Poeppel, David

AU - Miyauchi, Satoru

AU - Fujimaki, Norio

AU - Koizumi, Hideaki

AU - Miyashita, Yasushi

PY - 2000

Y1 - 2000

N2 - We have developed a method that incorporates the time-frequency characteristics of neural sources into magnetoencephalographic (MEG) source estimation. This method, referred to as the time-frequency multiple-signal- classification algorithm, allows the locations of neural sources to be estimated from any time-frequency region of interest. In this paper, we formulate the method based on the most general form of the quadratic time- frequency representations. We then apply it to two kinds of nonstationary MEG data: gamma-band (frequency range between 30-100 Hz) auditory activity data and spontaneous MEG data. Our method successfully detected the gamma-band source slightly medial to the N1m source location. The method was able to selectively localize sources for alpha-rhythm bursts at different locations. It also detected the mu-rhythm source from the alpha-rhythm-dominant MEG data that was measured with the subject's eyes closed. The results of these applications validate the effectiveness of the time-frequency MUSIC algorithm for selectively localizing sources having different time-frequency signatures.

AB - We have developed a method that incorporates the time-frequency characteristics of neural sources into magnetoencephalographic (MEG) source estimation. This method, referred to as the time-frequency multiple-signal- classification algorithm, allows the locations of neural sources to be estimated from any time-frequency region of interest. In this paper, we formulate the method based on the most general form of the quadratic time- frequency representations. We then apply it to two kinds of nonstationary MEG data: gamma-band (frequency range between 30-100 Hz) auditory activity data and spontaneous MEG data. Our method successfully detected the gamma-band source slightly medial to the N1m source location. The method was able to selectively localize sources for alpha-rhythm bursts at different locations. It also detected the mu-rhythm source from the alpha-rhythm-dominant MEG data that was measured with the subject's eyes closed. The results of these applications validate the effectiveness of the time-frequency MUSIC algorithm for selectively localizing sources having different time-frequency signatures.

KW - Biomagnetism

KW - Biomedical signal processing

KW - Functional brain imaging

KW - Inverse problems

KW - Time-frequency analysis

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Estimating neural sources from each time-frequency component of magnetoencephalographic data

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Keywords

ASJC Scopus subject areas

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