Estimation of attractor dimension of EEG using singular value decomposition

This paper describes a novel application of singular value decomposition (SVD) of subsets of the phase-space trajectory for calculation of the attractor dimension of a small data set. A certain number of local centres (M) are chosen randomly on the attractor and an adequate number of nearest neighbours (q=50) are ordered around each centre.

The local intrinsic dimension of a local centre is determined by the number of significant singular values and the attractor dimension (D2) by the average of the local intrinsic dimensions of the local centres. The SVD method has been evaluated for model data and EEG. The results indicate that the SVD method is a reliable approach for estimation of attractor dimension at moderate signal to noise ratios.

The paper emphasises the importance of SVD approach to EEG analysis.