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Title: Biological Gender Classification from fMRI via Hyperdimensional Computing
Hyperdimensional (HD) computing is a brain-inspired form of computing based on the manipulation of high-dimensional vectors. Offering robust data representation and relatively fast learning, HD computing is a promising candidate for energy-efficient classification of biological signals. This paper describes the application of HD computing-based machine learning to the classification of biological gender from resting-state and task functional magnetic resonance imaging (fMRI) from the publicly available Human Connectome Project (HCP). The developed HD algorithm derives predictive features through mean dynamic functional connectivity (dFC) analysis. Record encoding is employed to map features onto hyperdimensional space. Utilizing adaptive retraining techniques, the HD computing-based classifier achieves an average biological gender classification accuracy of 87%, as compared to 84% achieved by edge entropy measure.  more » « less
Award ID(s):
1814759
PAR ID:
10318246
Author(s) / Creator(s):
;
Date Published:
Journal Name:
2021 55th Asilomar Conference on Signals, Systems, and Computers
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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