Complexity measures of event related potential surface Laplacian data calculated using the wavelet packet transform.
We describe a method to obtain estimates of EEG signal complexity using the well-established wavelet packet transform with best basis selection. In particular, we use the two-dimensional wavelet packet transform to obtain estimates of the complexity of two-dimensional images. This allows us to calculate complexity estimates of high-resolution brain potential maps generated from 61 scalp electrode Visual Oddball paradigm, grand-mean data. A significant reduction in the complexity of the surface Laplacian time-slices is observed during and after the Visual Potential 300 (P3) event for the target case, possibly as a result of increased spatial synchrony associated with visual-related tasks. We also present the results of a statistical analysis of the largest principal component of the time-varying complexity curves, for control, high-risk, and alcoholic groups of male subjects. Parametric and non-parametric analyses show differences in the complexity data which are significant between the control group and the alcoholic and high-risk groups.
Department of Psychiatry, SUNY Health Science Center at Brooklyn, 11203, USA.
Kevin
K
Jones
Henri
H
Begleiter
Bernice
B
Porjesz
Kongming
K
Wang
David
D
Chorlian
D000465:Algorithms; D004569:Electroencephalography; D019277:Entropy; D018913:Event-Related Potentials, P300 / Q000502:physiology*; D005072:Evoked Potentials, Auditory / Q000502:physiology*; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D011930:Reaction Time; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted