Proceedings of the 3rd International Conference on Computation for Science and Technology

Artifacts Removal of EEG Signals using Adaptive Principal Component Analysis

Authors
Arjon Turnip, Dwi Esti Kusumandari
Corresponding Author
Arjon Turnip
Available Online January 2015.
DOI
10.2991/iccst-15.2015.34How to use a DOI?
Keywords
Artifacts, EEG, noise, principal component analysis
Abstract

Analysis of EEG activity usually raises the problem of differentiating between genuine EEG activity which is introduced through a variety of external influence. These artifacts may affect the outcome of the EEG recording. In this paper, wavelet denoising and band pass filter for preprocessing and an adaptive principal component analysis based recursive least squares algorithm for extraction are proposed to remove the artifacts. The algorithm is designed to adaptively derive a relatively small number of decorrelated linear combinations of a set of random zero-mean variables while retaining as much of the information from the original variables as possible. The proposed method was tested in real EEG records acquired from eight subjects. The experimental result show that the proposed method can effectively remove the artifacts from all subjects.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 3rd International Conference on Computation for Science and Technology
Series
Advances in Computer Science Research
Publication Date
January 2015
ISBN
978-94-62520-46-2
ISSN
2352-538X
DOI
10.2991/iccst-15.2015.34How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Arjon Turnip
AU  - Dwi Esti Kusumandari
PY  - 2015/01
DA  - 2015/01
TI  - Artifacts Removal of EEG Signals using Adaptive Principal Component Analysis
BT  - Proceedings of the 3rd International Conference on Computation for Science and Technology
PB  - Atlantis Press
SP  - 171
EP  - 174
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccst-15.2015.34
DO  - 10.2991/iccst-15.2015.34
ID  - Turnip2015/01
ER  -