Estimating the relative phase-difference in EEG using the matched phase reassigned cross-spectrogram
(2021) In Master's Thesis in Mathematical Sciences FMSM01 20211Mathematical 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:
http://lup.lub.lu.se/student-papers/record/9074434
- author
- Åkesson, Maria LU
- supervisor
- organization
- course
- FMSM01 20211
- year
- 2021
- 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}}, }