Investigation of short-term changes in visual evoked potentials with windowed adaptive chirplet transform.
We propose a new application of the adaptive chirplet transform that involves partitioning signals into non-overlapping sequential segments. From these segments, the local time-frequency structures of the signal are estimated by using a four-parameter chirplet decomposition. Entitled the windowed adaptive chirplet transform (windowed ACT), this approach is applied to the analysis of visual evoked potentials (VEPs). It can provide a unified and compact representation of VEPs from the transient buildup to the steady-state portion with less computational cost than its non-windowed counterpart. This paper also details a method to select the optimal window length for signal segmentation. This approach will be useful for long-term signal monitoring as well as for signal feature extraction and data compression.
Cui
Jie
J
Institute of Biomaterials, University of Toronto, Toronto, ON, M5S 3G9, Canada. jie.cui@uth.tmc.edu
Wong
Willy
W
D000222:Adaptation, Physiological / Q000502:physiology; D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D009473:Neuronal Plasticity / Q000502:physiology; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D014793:Visual Cortex / Q000502:physiology*