Skip to main content
Cyber Security and Resilience Technology
Cyber Security and Resilience Technology
Main navigation
Home
People
All Profiles
Leadership Team
Affiliate Faculty
Research Scientists
Research Staff
Postdoctoral Fellows
Students
Team
Datasets
Startups
spectral density function
Bayesian Non-parametric Models for Time Series Decomposition
Guillermo C. Granados Garcia, Ph.D. Student, Statistics
Jan 5, 17:00
-
19:00
B1 R4214
spectral density function
The standard approach to analyzing brain electrical activity is to examine the spectral density function (SDF) and identify frequency bands, defined apriori, that have the most substantial relative contributions to the overall variance of the signal. However, a limitation of this approach is that the precise frequency and bandwidth of oscillations are not uniform across cognitive demands. Thus, these bands should not be arbitrarily set in any analysis. To overcome this limitation, we propose three Bayesian Non-parametric models for time series decomposition, which are data-driven approaches that identify (i) the number of prominent spectral peaks, (ii) the frequency peak locations, and (iii) their corresponding bandwidths (or spread of power around the peaks).
