Estimation of attractor dimension of EEG using singular value decomposition

Pradhan N, Dutt Narayana, Sadasivan Puthusserypady, Ramesh R G

Research output: Contribution to journalJournal articleResearchpeer-review


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 languageEnglish
Issue number1
Pages (from-to)21-38
Publication statusPublished - 1996
Externally publishedYes


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