TY  - JOUR
AV  - none
ID  - scholars14629
TI  - Online detection and removal of eye blink artifacts from electroencephalogram
KW  - Biomedical signal processing; Brain computer interface; C++ (programming language); Correlation methods
KW  -  Accuracy rate; Canonical correlation analyse; Canonical correlations analysis; Electroencephalogram; Electroencephalogram signals; Error rate; Eye-blink artifacts; Eyes-blink artifacts; Modified empirical mode decomposition (FastEMD); Specificity rates
KW  -  Electroencephalography
KW  -  article; artifact; computer language; controlled study; correlation analysis; diagnostic test accuracy study; electroencephalogram; empirical mode decomposition; human; human experiment; processing speed; sensitivity and specificity
N1  - cited By 0
N2  - The most prominent type of artifact contaminating electroencephalogram (EEG) signals are the eye blink (EB) artifacts, which could potentially lead to misinterpretation of the EEG signal. Online identification and elimination of eye blink artifacts are crucial in applications such a Brain-Computer Interfaces (BCI), neurofeedback, and epilepsy diagnosis. In this paper, algorithms that combine unsupervised eye blink artifact detection (eADA) with modified Empirical Mode Decomposition (FastEMD) and Canonical Correlation Analysis (CCA) are proposed, i.e., FastEMD-CCA2 and FastCCA, to automatically identify eye blink artifacts and remove them in an online setting. The average accuracy, sensitivity, specificity, and error rate for eye blink artifact removal with FastEMD-CCA2 is 97.9, 97.65, 99.22, and 2.1, respectively, validated on a Hitachi dataset with 60 EEG signals, consisting of more than 5600 eye blink artifacts. FastCCA achieved an average of 99.47, 99.44, 99.74, and 0.53 artifact removal accuracy, sensitivity, specificity, and error rate, respectively, validated on the Hitachi dataset too. FastEMD-CCA2 and FastCCA algorithms are developed and implemented in the C++ programming language, mainly to investigate the processing speed that these algorithms could achieve in a different medium. Analysis has shown that FastEMD-CCA2 and FastCCA took about 10.7 and 12.7 ms, respectively, on average to clean a 1-s length of EEG segment. As a result, they're a viable option for applications that require online removal of eye blink objects from EEG signals. © 2021 Elsevier Ltd
PB  - Elsevier Ltd
SN  - 17468094
Y1  - 2021///
VL  - 69
A1  - Egambaram, A.
A1  - Badruddin, N.
A1  - Asirvadam, V.S.
A1  - Begum, T.
A1  - Fauvet, E.
A1  - Stolz, C.
JF  - Biomedical Signal Processing and Control
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109200270&doi=10.1016%2fj.bspc.2021.102887&partnerID=40&md5=34175f1e16d89612fe4390aa99026421
ER  -