eprintid: 11325
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/13/25
datestamp: 2023-11-10 03:25:50
lastmod: 2023-11-10 03:25:50
status_changed: 2023-11-10 01:14:59
type: conference_item
metadata_visibility: show
creators_name: Egambaram, A.
creators_name: Badruddin, N.
creators_name: Asirvadam, V.S.
creators_name: Fauvet, E.
creators_name: Stolz, C.
creators_name: Begum, T.
title: Automated and Online Eye Blink Artifact Removal from Electroencephalogram
ispublished: pub
keywords: Brain computer interface; Image processing, Canonical correlation analysis; Electroencephalogram signals; Empirical Mode Decomposition; Eye-blink artifact removals; Eye-blink artifacts; Neural information; Off-line methods; Reconstruction error, Electroencephalography
note: cited By 3; Conference of 2019 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2019 ; Conference Date: 17 September 2019 Through 19 September 2019; Conference Code:157352
abstract: Eyeblink artifacts often contaminates electroencephalogram (EEG) signals, which could potentially confound EEG's interpretation. A lot offline methods are available to remove this artifact, but an online solution is required to remove eyeblink artifacts in near real time for EEG signal to be beneficial in applications such as brain computer interface, (BCI). In this work, approaches that combines unsupervised eyeblink artifact detection with Empirical Mode Decomposition (EMD) and Canonical Correlation Analysis (CCA) are proposed to automatically identify eyeblink artifacts and remove them in an online setting. The proposed approaches are analysed and evaluated in terms of artifact removal accuracy and ability of the approaches to retain neural information in an EEG signal. Analysis has discovered that the approaches have achieved more than 98 accuracy in detecting and removing eyeblink artifacts in real time. The approaches have produced very low reconstruction error as well, the least is 0.148 in average. These algorithms took about 12ms in average to clean a 1s length of EEG segment, which is fast enough to process the signals in real time. © 2019 IEEE.
date: 2019
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084755107&doi=10.1109%2fICSIPA45851.2019.8977797&partnerID=40&md5=84509823f77ab692eacda6e566bfb938
id_number: 10.1109/ICSIPA45851.2019.8977797
full_text_status: none
publication: Proceedings of the 2019 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2019
pagerange: 159-163
refereed: TRUE
isbn: 9781728133775
citation:   Egambaram, A. and Badruddin, N. and Asirvadam, V.S. and Fauvet, E. and Stolz, C. and Begum, T.  (2019) Automated and Online Eye Blink Artifact Removal from Electroencephalogram.  In: UNSPECIFIED.