Constrained ICA based ballistocardiogram and electro-oculogram artifacts removal from visual evoked potential EEG signals measured inside MRI

Authors:
Tahir Rasheed;Myung Ho In;Young-Koo Lee;Sungyoung Lee;Soo Yeol Lee;Tae-Seong Kim
Affiliations:
Dept. of Computer Engineering;Dept. of Biomedical Engineering, Kyung Hee University, Suwon, Republic of Korea;Dept. of Computer Engineering;Dept. of Computer Engineering;Dept. of Biomedical Engineering, Kyung Hee University, Suwon, Republic of Korea;Dept. of Biomedical Engineering, Kyung Hee University, Suwon, Republic of Korea
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

Citing 4
Cited 1

An information-maximization approach to blind separation and blind deconvolution

Neural Computation
A fast fixed-point algorithm for independent component analysis

Neural Computation
New approximations of differential entropy for independent component analysis and projection pursuit

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Approach and applications of constrained ICA

IEEE Transactions on Neural Networks

Development and estimation of ubiquitous unconstrained BCG monitoring system using wireless sensor node

Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the simultaneous acquisition of EEG and fMRI, analysis of EEG signals is a difficult task due to ballistocardiogram (BCG) and electro-oculogram (EOG) artifacts. It gets worse if evoked potentials are measured inside MRI for their minute responses in comparison to the spontaneous brain responses. In this paper, we propose a new method for removing both artifacts simultaneously from the evoked EEG signals acquired inside MRI using constrained Independent component analysis (cICA). With properly designed reference functions for the BCG and EOG artifacts as constraints, cICA identifies the independent components (ICs) corresponding to the artifacts. Then artifact-removed EEG signals are reconstructed after removing the identified ICs to obtain evoked potentials. To evaluate our proposed technique, we have removed the artifacts with cICA and the standard template subtraction technique and generated visual evoked potentials (VEPs) respectively which are compared to the VEPs obtained from EEG signals measured outside MRI. Our results indicate that our cICA technique performs better than the standard BCG artifact removal methods with some efficient features.