We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
用于构建脑磁图网络的信号提取方法.
- Authors
杨春兰; 吴文晓; 吴水才; 任洁钏
- Abstract
For the construction of MEG brain network, the source signal with the strongest power in each brain region is generally selected to represent its neural activity, which causes the loss of information. In this study, two improvement schemes were proposed for this problem: research methods based on superposition averaging and cluster-based research methods. To evaluate the scheme, 51 subjects were selected with MEG data under task state. Signals were extracted by using maximum power,superimposed average and clustering, respectively, from each band.The brain function network was constructed and clustered. Additionally, the characteristics of brain network constructed by the three methods were compared. Based on the results, the superposition averaging method acquired the highest accuracy, then the highest power method and clustering acquired lower accuracy. Brain network feature analysis found that those based on the method of superimposed average and maximum power have strong small world attributes, while brain networks constructed using clustering methods have weaker world attributes. From that, it is feasible to construct MEG brain network using the superimposed average and maximum power methods.
- Subjects
ARITHMETIC mean; CONSTRUCTION; SUPERPOSITION (Optics)
- Publication
Journal of Beijing University of Technology, 2020, Vol 46, Issue 7, p795
- ISSN
0254-0037
- Publication type
Article
- DOI
10.11936/bjutxb2018110029