A Smoother State Space Multitaper Spectrogram
Name
SongChakravarty_2018_EMBC18_1425_FI.pdf
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1.27 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Song, Andrew H.
Chakravarty, Sourish
Brown, Emery Neal
Date Issued
October 2018
Journal
40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Song, Andrew H. et al. "A Smoother State Space Multitaper Spectrogram." 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), July 2018, Honolulu, Hawaii, USA, Institute of Electrical and Electronics Engineers (IEEE), October 2018. © 2018 IEEE
Version
Author's final manuscript
Abstract
A recent work (Kim et al. 2018) has reported a novel statistical modeling framework, the State-Space Multitaper (SSMT) method, to estimate time-varying spectral representation of non-stationary time series data. It combines the strengths of the multitaper spectral (MT) analysis paradigm with that of state-space (SS) models. In this current work, we explore a variant of the original SSMT framework by imposing a smoothness promoting SS model to generate smoother estimates of power spectral densities for non-stationary data. Specifically, we assume that the continuous processes giving rise to observations in the frequencies of interest follow multiple independent Integrated Wiener Processes (IWP). We use both synthetic data and electroencephalography (EEG) data collected from a human subject under anesthesia to compare the IWP-SSMT with the SSMT method and demonstrate the former's utility in yielding smoother descriptions of underlying processes. The original SSMT and IWP-SSMT can co-exist as a part of a model selection toolkit for nonstationary time series data.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Picower Institute for Learning and Memory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
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Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/embc.2018.8512190
