2 citations · 3 across the 6 of their papers we have counts for
7 papers
Modeling EEG Spectral Features through Warped Functional Mixed Membership Models
Emma Landry, Damla Senturk, Shafali Jeste +3
A common concern in the field of functional data analysis is the challenge of temporal misalignment, which is typically addressed using curve registration methods. Currently, most…
Covariate Adjusted Functional Mixed Membership Models
Nicholas Marco, Damla Şentürk, Shafali Jeste +3
Mixed membership models are a flexible class of probabilistic data representations used for unsupervised and semi-supervised learning, allowing each observation to partially belong…
Flexible Regularized Estimation in High-Dimensional Mixed Membership Models
Nicholas Marco, Damla Şentürk, Shafali Jeste +3
Mixed membership models are an extension of finite mixture models, where each observation can partially belong to more than one mixture component. A probabilistic framework for mix…
Functional Mixed Membership Models
Nicholas Marco, Damla Şentürk, Shafali Jeste +3
Mixed membership models, or partial membership models, are a flexible unsupervised learning method that allows each observation to belong to multiple clusters. In this paper, we pr…
Bayesian Analysis of Multidimensional Functional Data
John Shamshoian, Damla Senturk, Shafali Jeste +1
Multi-dimensional functional data arises in numerous modern scientific experimental and observational studies. In this paper we focus on longitudinal functional data, a structured…
Region-Referenced Spectral Power Dynamics of EEG Signals: A Hierarchical Modeling Approach
Qian Li, John Shamshoian, Damla Senturk +4
Functional brain imaging through electroencephalography (EEG) relies upon the analysis and interpretation of high-dimensional, spatially organized time series. We propose to repres…
Inferring Brain Signals Synchronicity from a Sample of EEG Readings
Qian Li, Damla Senturk, Catherine A. Sugar +4
Inferring patterns of synchronous brain activity from a heterogeneous sample of electroencephalograms (EEG) is scientifically and methodologically challenging. While it is intuitiv…