2 citations · 7 across the 11 of their papers we have counts for
11 papers
A robust PPG foundation model using multimodal physiological supervision
Eloy Geenjaar, Vince Calhoun, Scott Daly +4
Photoplethysmography (PPG), a non-invasive measure of changes in blood volume, is widely used in both wearable devices and clinical settings. Recent PPG foundation models either us…
Mapping minds not averages: a scalable subject-specific manifold learning framework for neuroimaging data
Eloy Geenjaar, Vince Calhoun
Mental and cognitive representations are believed to reside on low-dimensional, non-linear manifolds embedded within high-dimensional brain activity. Uncovering these manifolds is…
CiTrus: Squeezing Extra Performance out of Low-data Bio-signal Transfer Learning
Eloy Geenjaar, Lie Lu
Transfer learning for bio-signals has recently become an important technique to improve prediction performance on downstream tasks with small bio-signal datasets. Recent works have…
Learning low-dimensional dynamics from whole-brain data improves task capture
Eloy Geenjaar, Donghyun Kim, Riyasat Ohib +4
The neural dynamics underlying brain activity are critical to understanding cognitive processes and mental disorders. However, current voxel-based whole-brain dimensionality reduct…
CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs
Eloy Geenjaar, Noah Lewis, Amrit Kashyap +2
Communication within or between complex systems is commonplace in the natural sciences and fields such as graph neural networks. The brain is a perfect example of such a complex sy…
Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes
Alex Fedorov, Eloy Geenjaar, Lei Wu +7
Recent neuroimaging studies that focus on predicting brain disorders via modern machine learning approaches commonly include a single modality and rely on supervised over-parameter…
Spatio-temporally separable non-linear latent factor learning: an application to somatomotor cortex fMRI data
Eloy Geenjaar, Amrit Kashyap, Noah Lewis +2
Functional magnetic resonance imaging (fMRI) data contain complex spatiotemporal dynamics, thus researchers have developed approaches that reduce the dimensionality of the signal w…
Variational voxelwise rs-fMRI representation learning: Evaluation of sex, age, and neuropsychiatric signatures
Eloy Geenjaar, Tonya White, Vince Calhoun
We propose to apply non-linear representation learning to voxelwise rs-fMRI data. Learning the non-linear representations is done using a variational autoencoder (VAE). The VAE is…
Fusing multimodal neuroimaging data with a variational autoencoder
Eloy Geenjaar, Noah Lewis, Zening Fu +3
Neuroimaging studies often involve the collection of multiple data modalities. These modalities contain both shared and mutually exclusive information about the brain. This work ai…
Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data
Alex Fedorov, Eloy Geenjaar, Lei Wu +3
Self-supervised learning has enabled significant improvements on natural image benchmarks. However, there is less work in the medical imaging domain in this area. The optimal model…
Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer's Disease
Alex Fedorov, Tristan Sylvain, Eloy Geenjaar +7
Sensory input from multiple sources is crucial for robust and coherent human perception. Different sources contribute complementary explanatory factors. Similarly, research studies…