Abstract
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 (D-2) 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.
Original language | English |
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Journal | Sadhana |
Volume | 21 |
Issue number | 1 |
Pages (from-to) | 21-38 |
ISSN | 0256-2499 |
DOIs | |
Publication status | Published - 1996 |
Externally published | Yes |