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题名: Local region structured noise reduction for cortical optical imaging
作者: Liu YD(刘亚东); Hu DW(胡德文); Zhou ZT(周宗潭); Liu FY(刘发益)
作者部门: 机器人学研究室
关键词: Optical imaging ; Structured noise ; Canonical correlation analysis ; Monte-Carlo simulation ; Multi-taper method
刊名: NEUROCOMPUTING
ISSN号: 0925-2312
出版日期: 2009
卷号: 72, 期号:10-12, 页码:2242-2248
收录类别: SCI ; EI
产权排序: 1
摘要: In this paper, we proposed a local region structured noise reduction method for cortical optical imaging (01). In our method, block-designed task paradigm was employed. Canonical correlation analysis (CCA) technique was used to extract the underlying structured sources voxel by voxel. The response signals were detected among structured sources by surrogate test based on the reduced autoregression model (ST-RARM) technique. The power of structured noise was eliminated from original time series and then the data were reconstructed. Monte-Carlo simulation was applied to demonstrate the validity of our method. The results showed that our method was more efficient in activated voxel detection compared to the generally used methods PCA, DCT. Further, by using our method the phase knowledge of response signals was well preserved in the reconstructed data and hence a more accurate estimate was obtained. The final activity mapping was generated by utilizing the knowledge of both response amplitude and phase. The vein artifacts were efficiently reduced. Six sets of true 01 data collected from the hind-paw (HP) area of rat's cortex were processed and improved activity mappings were obtained.
语种: 英语
WOS记录号: WOS:000266702300020
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/10135
Appears in Collections:机器人学研究室_期刊论文

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