Optimal Time-Frequency analysis of the multiple time-translated locally stationary processes
(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:
https://lup.lub.lu.se/record/4175274
- author
- Brynolfsson, Johan LU and Sandsten, Maria LU
- organization
- publishing date
- 2013
- 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}}, }