4.6 Article

Scalable and Robust Tensor Decomposition of Spontaneous Stereotactic EEG Data

期刊

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 66, 期 6, 页码 1549-1558

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2018.2875467

关键词

Tensor decomposition; dynamic functional connectivity; stereotactic EEG; optimization

资金

  1. National Institutes of Health [R01-NS074980, R01-EB026299, R01-NS089212]

向作者/读者索取更多资源

Objective: Identification of networks from resting brain signals is an important step in understanding the dynamics of spontaneous brain activity. We approach this problem using a tensor-based model. Methods: We develop a rank-recursive scalable and robust sequential canonical polyadic decomposition (SRSCPD) framework to decompose a tensor into several rank-1 components. Robustness and scalability are achieved using a warm start for each rank based on the results from the previous rank. Results: In simulations we show that SRSCPD consistently outperforms the multi-start alternating least square (ALS) algorithm over a range of ranks and signal-to-noise ratios (SNRs), with lower computation cost. When applying SRSCPD to resting in-vivo stereotactic EEG (SEEG) data from two subjects with epilepsy, we found components corresponding to default mode and motor networks in both subjects. These components were also highly consistent within subject between two sessions recorded several hours apart. Similar components were not obtained using the conventional ALS algorithm. Conclusion: Consistent brain networks and their dynamic behaviors were identified from resting SEEG data using SRSCPD. Significance: SRSCPD is scalable to large datasets and therefore a promising tool for identification of brain networks in long recordings from single subjects.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据