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Coherence Estimation between EEG signals using Multiple Window Time-Frequency Analysis compared to Gaussian Kernels

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Author

Summary, in English

It is believed that neural activity evoked by cognitive tasks is spatially correlated in certain frequency bands. The electroencephalogram (EEG) is highly affected by noise of large amplitude which calls for sophisticated time local coherence estimation methods.







In this paper we investigate different approaches to estimate time local coherence between two real valued signals. Our results indicate that the method using two dimensional Gaussian kernels has a slightly better average SNR compared to the multiple window approach. On the other hand, the multiple window approach has a more narrow SNR distribution and seems to perform better in the worst case.

Department/s

Publishing year

2006

Language

English

Publication/Series

14th European Signal Processing Conference

Document type

Conference paper

Publisher

IEEE - Institute of Electrical and Electronics Engineers Inc.

Topic

  • Probability Theory and Statistics

Keywords

  • Non-stationary
  • Time-Frequency-Analysis
  • EEG
  • Coherence

Conference name

14th European Signal Processing Conference (EUSIPCO 2006)

Conference date

2006-09-04 - 2006-09-08

Conference place

Florence, Italy

Status

Published

Research group

  • Statistical Signal Processing Group