Model of independent component analysis with reference curve and its application in removing artifact from electroencephalograph

DOI

10.2991/iiicec-15.2015.274How to use a DOI?

Keywords

Artifacts removal; Electroencephalogram; Independent Component Analysis with Reference Curve

Abstract

This paper proposes a model named Independent Component Analysis with Reference Curve (ICARC) to extract and remove artifact signal from Electroencephalogram (EEG). Firstly, an additional requirement and a priori information are introduced directly into the contrast function of the traditional ICA model. Then, an augmented Lagrangian function is formed based on this new model. Finally, the iterative solution is calculated by using the Newton iterative method. The simulations and experiments are implemented to indicate the performance of our model comparing with other method. The results show that: 1) more stable results are given by our model; 2) higher precision is obtained in the results by the ICARC model.

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  - Lizhi Cui
AU  - Zhihao Ling
AU  - Josiah Poon
AU  - Simon K. Poon
PY  - 2015/03
DA  - 2015/03
TI  - Model of independent component analysis with reference curve and its application in removing artifact from electroencephalograph
BT  - Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference
PB  - Atlantis Press
SP  - 1241
EP  - 1245
SN  - 2352-538X
UR  - https://doi.org/10.2991/iiicec-15.2015.274
DO  - 10.2991/iiicec-15.2015.274
ID  - Cui2015/03
ER  -