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- Title
EMPIRICAL MODE DECOMPOSITION METHOD FOR MEG PHANTOM DATA ANALYSIS.
- Authors
YANG, JUHONG; SAITO, YUKI; SHI, QIWEI; CAO, JIANTING; TANAKA, TOSHIHISA; TAKEDA, TSUNEHIRO
- Abstract
Magnetoencephalography (MEG) is a powerful and non-invasive technique for measuring human brain activity with a high temporal resolution. The motivation for studying MEG data analysis is to extract the essential features from real-world measured data and represent them corresponding to the human brain functions. This usually depends on how to reduce a high level noise from the measurement. In this paper, a novel multistage MEG data analysis method based on the empirical mode decomposition (EMD) and independent component analysis (ICA) approaches is proposed for the feature extraction. Moreover, EMD and ICA algorithms are investigated for analyzing the MEG single-trial data which is recorded from the experiment of phantom. The analyzed results are presented to illustrate the effectiveness and high performance both in high level noise reduction by EMD associated with ICA approach and source localization by equivalent current dipole fitting method.
- Subjects
HILBERT-Huang transform; MAGNETOENCEPHALOGRAPHY; BRAIN research; NEUROPHYSIOLOGY; INDEPENDENT component analysis
- Publication
Journal of Circuits, Systems & Computers, 2009, Vol 18, Issue 8, p1467
- ISSN
0218-1266
- Publication type
Article
- DOI
10.1142/S0218126609005794