Scalable and Robust Tensor Decomposition of Spontaneous Stereotactic EEG Data.

ABSTRACT:

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

SUBMITTER: Jian Li 

PROVIDER: S-EPMC6677658 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature