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Estimating the relative phase-difference in EEG using the matched phase reassigned cross-spectrogram

Åkesson, Maria LU (2021) In Master's Thesis in Mathematical Sciences FMSM01 20211
Mathematical Statistics
Abstract
In brain connectivity research the relative phase between two EEG oscillations is particularly relevant, as it could provide information about the conduction delay between two regions. However, due to EEG signals containing large amounts of noise and spurious connections, the relative phase is seldom estimated. The aim of this thesis is to explore the possibilities of estimating relative phase using an algorithm based of the scaled reassigned spectrogram (ScRe-Spec) and the matched phase reassignment (MPR). Through simulations it is shown that both the Rényi entropy and the time-frequency concentration are suitable methods for evaluating the reassigned spectrograms. The algorithm is shown to give more correct estimations in comparison to... (More)
In brain connectivity research the relative phase between two EEG oscillations is particularly relevant, as it could provide information about the conduction delay between two regions. However, due to EEG signals containing large amounts of noise and spurious connections, the relative phase is seldom estimated. The aim of this thesis is to explore the possibilities of estimating relative phase using an algorithm based of the scaled reassigned spectrogram (ScRe-Spec) and the matched phase reassignment (MPR). Through simulations it is shown that both the Rényi entropy and the time-frequency concentration are suitable methods for evaluating the reassigned spectrograms. The algorithm is shown to give more correct estimations in comparison to other relative-phase estimation methods when the signal to noise ratio is low. Lastly, when tested on two real EEG-data examples, the algorithm shows promising results. (Less)
Please use this url to cite or link to this publication:
author
Åkesson, Maria LU
supervisor
organization
course
FMSM01 20211
year
type
H2 - Master's Degree (Two Years)
subject
publication/series
Master's Thesis in Mathematical Sciences
report number
LUTFMS-34231-2021
ISSN
1404-6342
other publication id
2021:E63
language
English
id
9074434
date added to LUP
2022-02-02 16:09:20
date last changed
2022-02-02 16:09:20
@misc{9074434,
  abstract     = {{In brain connectivity research the relative phase between two EEG oscillations is particularly relevant, as it could provide information about the conduction delay between two regions. However, due to EEG signals containing large amounts of noise and spurious connections, the relative phase is seldom estimated. The aim of this thesis is to explore the possibilities of estimating relative phase using an algorithm based of the scaled reassigned spectrogram (ScRe-Spec) and the matched phase reassignment (MPR). Through simulations it is shown that both the Rényi entropy and the time-frequency concentration are suitable methods for evaluating the reassigned spectrograms. The algorithm is shown to give more correct estimations in comparison to other relative-phase estimation methods when the signal to noise ratio is low. Lastly, when tested on two real EEG-data examples, the algorithm shows promising results.}},
  author       = {{Åkesson, Maria}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Thesis in Mathematical Sciences}},
  title        = {{Estimating the relative phase-difference in EEG using the matched phase reassigned cross-spectrogram}},
  year         = {{2021}},
}