Paper Info
Title
A method for determinism in short time series, and its application to stationary EEG.
Abstract
A novel method for detecting determinism in short time series is developed and applied to investigate determinism in stationary electroencephalogram (EEG) recordings. This method is based on the observation that the trajectory of a time series generated from a differentiable dynamical system behaves smoothly in an embedded state space. The angles between two successive tangent vectors in the trajectory reconstructed from the time series is calculated as a function of time. The irregularity of the angle variations obtained from the time series is estimated using second-order difference plots, and compared with that of the corresponding surrogate data. Using this method, we demonstrate that scalp EEG recordings from normal subjects do not exhibit a low-dimensional deterministic structure. This method can be useful for analyzing determinism in short time series, such as those from physiological recordings.
Year
DOI
Venue
2002
10.1109/TBME.2002.804581
IEEE Trans. Biomed. Engineering
Keywords
Field
DocType
scalp eeg recordings,angle variations irregularity,electrodiagnostics,medical signal detection,electroencephalography,embedded state space,differentiable dynamical system,time series trajectory,successive tangent vectors,stationary electroencephalogram recordings,stationary eeg,short time series determinism,time series,vectors,physiological recordings,dynamic system,state space,second order,surrogate data
Computer vision,Determinism,Tangent vector,Algorithm,Differentiable function,Artificial intelligence,Smoothness,Surrogate data,State space,Trajectory,Mathematics,Dynamical system
Journal
Volume
Issue
ISSN
49
11
0018-9294
Citations 
PageRank 
References 
8
1.90
2
Authors
3
Name
Order
Citations
PageRank
Jaeseung Jeong19517.93
John C Gore261641.36
Bradley S Peterson311516.96