eprintid: 15371
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/53/71
datestamp: 2023-11-10 03:29:59
lastmod: 2023-11-10 03:29:59
status_changed: 2023-11-10 01:59:19
type: conference_item
metadata_visibility: show
creators_name: Al-Ezzi, A.
creators_name: Kamel, N.
creators_name: Al-Shargabi, A.
creators_name: Yahya, N.
creators_name: Faye, I.
creators_name: Al-Hiyali, M.I.
title: SVD-Based Feature Extraction Technique for the Improvement of Effective Connectivity Detection
ispublished: pub
keywords: Biomedical signal processing; Brain; Electroencephalography; Extraction; Feature extraction; Independent component analysis; Machine learning; Singular value decomposition, Brain functions; Effective connectivities; Electrical activities; Electroencephalogram analysis; Feature extraction techniques; Mental state; Neurofeedback; Partial directed coherence; Social anxieties; Social anxiety disorder, Data mining
note: cited By 2; Conference of 2021 International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2021 ; Conference Date: 1 December 2021 Through 2 December 2021; Conference Code:176965
abstract: Electroencephalogram (EEG) plays an essential part in identifying brain function and behaviors for different mental states. Nevertheless, the captured electrical activity is always found to be contaminated with various artifacts that negatively influence the accuracy of EEG analysis. Therefore, it is crucial to build a model to constructively identify and extract clean EEG recordings during the investigation of the dynamical brain networks. To improve the estimation of effective connectivity (EC) and EEG signal denoising, an EEG decomposition method based on the singular value decomposition (SVD) analysis was proposed. The main purpose of the decomposition is to create a method to estimate a signal that represents most of the principal components of the information contained in each brain region before calculating the partial directed coherence (PDC). SVD-based technique and PDC were used to quantify the causal influence of default mode network (DMN) regions on each other and track the changes in brain connectivity. Results of statistical analysis on the effective connectivity using the SVD-PDC algorithm have shown to better reflect the flow of causal information than the independent component analysis (ICA)-PDC. The hybrid algorithm (SVD-PDC) is proposed in this work as an alternative robust adaptive feature extraction method for EEG signals to improve the detection of brain effective connectivity. © 2021 IEEE.
date: 2021
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126585193&doi=10.1109%2fICICyTA53712.2021.9689141&partnerID=40&md5=b0aad16f6a6b52a4a57c82de24ca3bc5
id_number: 10.1109/ICICyTA53712.2021.9689141
full_text_status: none
publication: 2021 International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2021
pagerange: 52-57
refereed: TRUE
isbn: 9781665417778
citation:   Al-Ezzi, A. and Kamel, N. and Al-Shargabi, A. and Yahya, N. and Faye, I. and Al-Hiyali, M.I.  (2021) SVD-Based Feature Extraction Technique for the Improvement of Effective Connectivity Detection.  In: UNSPECIFIED.