DySCo: A general framework for dynamic functional connectivity
Fig 3
Computational efficiency of the DySCo framework.
i) Comparison of computational speed of the TCEVD algorithm compared to naïve numerical methods (the MATLAB eigs function, see Investigation of computational efficiency of the TCEVD in the DySCo framework), using randomly generated covariance matrices in a window of size 10. We repeated the experiment 20 times. Thick lines represent the mean computation time, thin lines the ± variance. ii) Comparison of the memory requirements (in bytes) for the storage of the matrices using their upper triangular form (N ( N − 1 ) ∕ 2), versus using the eigenvector decomposition (NT). iii) Comparison of the time required to compute the Euclidean distance between two vectorized matrices versus using the DySCo EVD approach.