Extracting Feature Vectors From Event-Related fMRI Data to Enable Machine Learning Analysis

Linear models are the dominant means of extracting summaries of events in fMRI for feature vector based machine learning. While they are both useful and robust, they are limited by the assumptions made in modeling. In this work, we examine a number of feature extraction techniques adjacent to linear models that account for or allow wider variation. Primarily, we construct mixed effects models able to account for variation between stimuli of the same class and perform empirical tests on the resulting feature extraction – classifier system. We extend this analysis to spatial temporal models as well as summary models. We find that mixed effects models increase classifier performance at the cost of increased uncertainty in prediction estimates. In addition, these models identify similar regions of interest in separating classes. While they currently require knowledge hidden during testing, we present these results as an optimum to be reached in additional works.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Biomedical Engineering

Department dc:contributor.department

Department of Biomedical Engineering and Mechanics

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2022

Author dc:creator

• Soldate, Jeffrey S.

Chair dc:contributor.committeechair

• LaConte, Stephen M.

Committee members dc:contributor.committeemember

• VandeVord, Pamela J.
• Montague, P. Read
• Casas, Brooks
• Vijayan, Sujith

• fMRI
• Machine Learning
• Feature Selection
• Mixed Effects Models

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:35646

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/112090