# Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction

arXiv: https://arxiv.org/abs/1703.01842 · HTML: https://mlsanity.com/paper/1703.01842/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2017.

## Built on by (most cited first, 7 of 7)

- [SoK: Differential Privacy on Graph-Structured Data](https://mlsanity.com/paper/2203.09205/) (2022)
- [Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities](https://mlsanity.com/paper/2211.02624/) (2022)
- [BLIS-Net: Classifying and Analyzing Signals on Graphs](https://mlsanity.com/paper/2310.17579/) (2023)
- [Learning Graph Filters for Structure-Function Coupling based Hub Node Identification](https://mlsanity.com/paper/2410.17410/) (2024)
- [Graph-LDA: Graph Structure Priors to Improve the Accuracy in Few-Shot Classification](https://mlsanity.com/paper/2108.10427/) (2021)
- [Joint Data Inpainting and Graph Learning via Unrolled Neural Networks](https://mlsanity.com/paper/2407.11429/) (2024)
- [Comparing and Scaling fMRI Features for Brain-Behavior Prediction](https://mlsanity.com/paper/2507.20601/) (2025)

## Abstract (arXiv)

Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies between brain signals. In the present work, we apply dimensionality reduction techniques based on graph representations of the brain to decode brain activity from real and simulated fMRI datasets. We introduce seven graphs obtained from a) geometric structure and/or b) functional connectivity between brain areas at rest, and compare them when performing dimension reduction for classification. We show that mixed graphs using both a) and b) offer the best performance. We also show that graph sampling methods perform better than classical dimension reduction including Principal Component Analysis (PCA) and Independent Component Analysis (ICA).