Filtering of functional near infrared spectroscopy signals by eigenvalue based methods

Author

Eken, A.

Author_Institution

Enformatik Enstitusu, Med. Enformatik Ana Bilim Dali, Orta Dogu Teknik Univ., Ankara, Turkey

fYear

2014

fDate

23-25 April 2014

Firstpage

373

Lastpage

376

Abstract

Functional Near Infrared Spectroscopy is used in neuroimaging studies to observe the oxyhemoglobin (HBO2) and deoxyhemoglobin (HB) changes. Blood oxygen level dependency (BOLD) signal that is collected by using this system shows response in related region in brain against an applied stimulus. Therefore in these signals, signal to noise ratio (SNR) is quite important to decide the behavior of brain in related region In this study, fNIRS data was filtered by using eigenvalue based methods such as Principal Component Analysis (PCA) and Truncated Singular Value Decomposition (tSVD). Using SNR and Autoregressive (AR) power spectrum performance results were compared.

Keywords

eigenvalues and eigenfunctions; filtering theory; medical signal processing; principal component analysis; singular value decomposition; BOLD signal; PCA; blood oxygen level dependency signal; eigenvalue based methods; fNIRS data; functional near infrared spectroscopy signals; neuroimaging studies; principal component analysis; signal filtering; tSVD; truncated singular value decomposition; Art; Conferences; Matrix decomposition; Principal component analysis; Signal to noise ratio; Spectroscopy; AR Power Spectrum; PCA; fNIRS; tSVD;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference (SIU), 2014 22nd

Conference_Location

Trabzon

Type

conf

DOI

10.1109/SIU.2014.6830243

Filename

6830243