Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Optimal Time-Frequency analysis of the multiple time-translated locally stationary processes

Brynolfsson, Johan LU and Sandsten, Maria LU (2013) 21st European Signal Processing Conference (EUSIPCO 2013)
Abstract
A previously proposed model for non-stationary signals is

extended in this contribution. The model consists of mul-

tiple time-translated locally stationary processes. The opti-

mal Ambiguity kernel for the process in mean-square-error

sense is computed analytically and is used to estimate the

time-frequency distribution. The performance of the kernel

is compared with other commonly used kernels. Finally the

model is applied to electrical signals from the brain (EEG)

measured during a concentration task.
Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Time frequency analysis, Locally stationary process, Optimal Ambiguity kernel, EEG.
host publication
[Host publication title missing]
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
21st European Signal Processing Conference (EUSIPCO 2013)
conference location
Marrakech, Morocco
conference dates
2013-09-09 - 2013-09-13
external identifiers
  • wos:000341754500163
language
English
LU publication?
yes
id
8951198c-2a38-473b-bf88-ee81d6390523 (old id 4175274)
date added to LUP
2016-04-04 12:01:03
date last changed
2018-11-21 21:08:32
@inproceedings{8951198c-2a38-473b-bf88-ee81d6390523,
  abstract     = {{A previously proposed model for non-stationary signals is<br/><br>
extended in this contribution. The model consists of mul-<br/><br>
tiple time-translated locally stationary processes. The opti-<br/><br>
mal Ambiguity kernel for the process in mean-square-error<br/><br>
sense is computed analytically and is used to estimate the<br/><br>
time-frequency distribution. The performance of the kernel<br/><br>
is compared with other commonly used kernels. Finally the<br/><br>
model is applied to electrical signals from the brain (EEG)<br/><br>
measured during a concentration task.}},
  author       = {{Brynolfsson, Johan and Sandsten, Maria}},
  booktitle    = {{[Host publication title missing]}},
  keywords     = {{Time frequency analysis; Locally stationary process; Optimal Ambiguity kernel; EEG.}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Optimal Time-Frequency analysis of the multiple time-translated locally stationary processes}},
  year         = {{2013}},
}