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

Kensuke Sekihara, Srikantan Nagarajan, David Poeppel, Yasushi Miyashita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We have developed a method that incorporates the time-frequency characteristics of neural sources into magnetoencephalographic (MEG) source estimation. This method calculates a time-frequency matrix in which diagonal and off-diagonal terms are the auto- and cross-time-frequency distributions of multi-channel MEG recordings, respectively. The method averages this matrix over the time-frequency region of interest. The locations of neural sources are then estimated by checking the orthogonality between the noise subspace of this averaged matrix and the sensor lead field. The method therefore allows neural sources to be estimated from any time-frequency component of measured data. We applied the method to estimating sources for gamma-band (frequency range between 30 and 100 Hz) auditory activity, and the results demonstrating the method's effectiveness were obtained.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis
PublisherIEEE
Pages69-72
Number of pages4
StatePublished - 1998
EventProceedings of the 1998 IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis - Pittsburgh, PA, USA
Duration: Oct 6 1998Oct 9 1998

Other

OtherProceedings of the 1998 IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis
CityPittsburgh, PA, USA
Period10/6/9810/9/98

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Estimating neural sources from each time-frequency component of magnetoencephalographic data'. Together they form a unique fingerprint.

Cite this