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

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 languageEnglish
JournalSadhana
Volume21
Issue number1
Pages (from-to)21-38
ISSN0256-2499
DOIs
Publication statusPublished - 1996
Externally publishedYes

Cite this

N, Pradhan ; Narayana, Dutt ; Puthusserypady, Sadasivan ; R G, Ramesh. / Estimation of attractor dimension of EEG using singular value decomposition. In: Sadhana. 1996 ; Vol. 21, No. 1. pp. 21-38.
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author = "Pradhan N and Dutt Narayana and Sadasivan Puthusserypady and {R G}, Ramesh",
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Estimation of attractor dimension of EEG using singular value decomposition. / N, Pradhan; Narayana, Dutt; Puthusserypady, Sadasivan; R G, Ramesh.

In: Sadhana, Vol. 21, No. 1, 1996, p. 21-38.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Estimation of attractor dimension of EEG using singular value decomposition

AU - N, Pradhan

AU - Narayana, Dutt

AU - Puthusserypady, Sadasivan

AU - R G, Ramesh

PY - 1996

Y1 - 1996

N2 - 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.

AB - 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.

U2 - 10.1007/BF02781785

DO - 10.1007/BF02781785

M3 - Journal article

VL - 21

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EP - 38

JO - Sadhana

JF - Sadhana

SN - 0256-2499

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