Skip to main content
Communication and Computing Systems Lab
CCSL
Communication and Computing Systems Lab
Home
People
All Profiles
Principal Investigator
Postdoctoral Fellows
Research Scientists
Research Staff
Students
Alumni
Former Members
Research
Wireless Communication
Body Area Network
AI Accelerator
All Projects
Publications
Publications
Google Scholar
DBLP
IEEE Xplore
KAUST Repository
ORCID
Events
Media Gallery
Contacts
Join us
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).