Normalized Cumulative Periodogram Method for Tuning a Regularization Parameter of the EEG Inverse Problem

Mohammed J. Aburidi; Adnan Salman

doi:10.12816/0045064

Article Details

Normalized Cumulative Periodogram Method for Tuning a Regularization Parameter of the EEG Inverse Problem

Authors:

Mohammed J. Aburidi | Adnan Salman

Publisher:

JOMENAS Press

Main Topics:

Technology | Data processing and computer science

Journal:

The Journal of Middle East and North Africa Sciences 2018 Vol.4 Issue 2, pp.23-32

Document Type:

Article

Publication Date:

2/2018

Document Language:

English

Document ID (Org No.):

114620

Frequency:

Monthly

eISSN:

24128937

pISSN:

24129763

DOI:

10.12816/0045064

Descriptors:

Keywords:

Abstract:

 Investigation of the functional neuronal activity in the human brain depends on the localization ofElectroencephalographic (EEG) signals to their cortex sources, which requires solving the source localization inverseproblem. The problem is ill-conditioned and under-determinate, and so it is ill-posed. To find a treatment of the ill-posednature of the problem, a regularization scheme must be applied. A crucial issue in the application of any regularizationscheme, in any domain, is the optimal selection of the regularization parameter. The selected regularization parameter has tofind an optimal tradeoff between the data fitting term and the amount of regularization. Several methods exist for finding anoptimal estimate of the regularization parameter of the ill-posed problems in general. In this paper, authors investigate thenormalized cumulative periodogram (NCP) and apply it to the source localization problem. Furthermore, authors compareits performance with other two parameter choice methods which are L-curve and Generalized-Cross Validation (GCV) interms of accuracy and reliability. Authors opted the WMNE algorithm to solve the EEG inverse problem with the applicationof different noise levels and different simulated source generators. Our results indicate that NCP method gives the bestestimation for the regularization parameter in general. However, for some levels of noise, GCV method has similarperformance. In contrast, both NCP and GCV methods outperform the L-curve method and resulted in a better averagelocalization error.

Article Details

Normalized Cumulative Periodogram Method for Tuning a Regularization Parameter of the EEG Inverse Problem

Authors:

Mohammed J. Aburidi | Adnan Salman

Publisher:

JOMENAS Press

Main Topics:

Technology | Data processing and computer science

Journal:

The Journal of Middle East and North Africa Sciences 2018 Vol.4 Issue 2, pp.23-32

Document Type:

Article

Publication Date:

2/2018

Document Language:

English

Document ID (Org No.):

114620

Frequency:

Monthly

eISSN:

24128937

pISSN:

24129763

DOI:

10.12816/0045064

Descriptors:

ELECTROENCEPHALOGRAPHS

SIGNAL PROCESSING

LINEAR EQUATIONS

NERVES

SIMULATION

Source Localization

EEG Forward Problem

Weighted Minimum Norm Estimate

Head Modeling

EEG Regularization Parameter

Issues

Article Details

Authors: Mohammed J. Aburidi | Adnan Salman

Publisher: JOMENAS Press

Main Topics: Technology | Data processing and computer science

Journal: The Journal of Middle East and North Africa Sciences 2018 Vol.4 Issue 2, pp.23-32

Document Type: Article

Publication Date: 2/2018

Document Language: English

Document ID (Org No.): 114620

Frequency: Monthly

eISSN: 24128937

pISSN: 24129763

DOI: 10.12816/0045064

Descriptors:

Issues

Authors:

Mohammed J. Aburidi | Adnan Salman

Publisher:

JOMENAS Press

Main Topics:

Technology | Data processing and computer science

Journal:

The Journal of Middle East and North Africa Sciences 2018 Vol.4 Issue 2, pp.23-32

Document Type:

Article

Publication Date:

2/2018

Document Language:

English

Document ID (Org No.):

114620

Frequency:

Monthly

eISSN:

24128937

pISSN:

24129763

DOI:

10.12816/0045064