Artifacts Removal of EEG Signals using Adaptive Principal Component Analysis

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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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  -