TY - GEN
T1 - Random segmentation based principal component analysis to remove residual MR gradient artifact in the simultaneous EEG/fMRI
T2 - 20th International Conference on Neural Information Processing, ICONIP 2013
AU - Kim, Hyun Chul
AU - Lee, Jong Hwan
PY - 2013
Y1 - 2013
N2 - In the electroencephalography (EEG) data simultaneously acquired with the functional magnetic resonance imaging (fMRI) data, the removal of the residual magnetic resonance (MR) gradient artifacts has been a challenging issue. To remove gradient artifacts generated from switching MR gradient field, average artifact subtraction (AAS) has been widely used. After applying the AAS method, however, residual MR gradient artifacts still remained in corrected EEG data. In this study, we proposed a novel method to remove the residual MR gradient artifacts (GAs) using random segmentation based principal component analysis (rsPCA). The performance of rsPCA was compared to that of the independent component analysis (ICA) method using data acquired from a motor imagery task. The results indicated that rsPCA could suppress further the residual MR gradient artifacts remained from the AAS step compared to the ICA method.
AB - In the electroencephalography (EEG) data simultaneously acquired with the functional magnetic resonance imaging (fMRI) data, the removal of the residual magnetic resonance (MR) gradient artifacts has been a challenging issue. To remove gradient artifacts generated from switching MR gradient field, average artifact subtraction (AAS) has been widely used. After applying the AAS method, however, residual MR gradient artifacts still remained in corrected EEG data. In this study, we proposed a novel method to remove the residual MR gradient artifacts (GAs) using random segmentation based principal component analysis (rsPCA). The performance of rsPCA was compared to that of the independent component analysis (ICA) method using data acquired from a motor imagery task. The results indicated that rsPCA could suppress further the residual MR gradient artifacts remained from the AAS step compared to the ICA method.
KW - Electroencephalography
KW - Functional magnetic resonance imaging
KW - MR gradient artifact
KW - Principal component analysis
KW - Random segmentation
KW - Simultaneous EEG/fMRI
UR - https://www.scopus.com/pages/publications/84893346371
U2 - 10.1007/978-3-642-42051-1_14
DO - 10.1007/978-3-642-42051-1_14
M3 - Conference contribution
AN - SCOPUS:84893346371
SN - 9783642420504
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 101
EP - 107
BT - Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
Y2 - 3 November 2013 through 7 November 2013
ER -