# Robust Sparse Bayesian Learning Based on Minimum Error Entropy for Noisy High-Dimensional Brain Activity Decoding

arXiv: https://arxiv.org/abs/2508.11657 · HTML: https://mlsanity.com/paper/2508.11657/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 70 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 14 of 14)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [A General Framework for Updating Belief Distributions](https://mlsanity.com/paper/1306.6430/) (2013)
- [Generic decoding of seen and imagined objects using hierarchical visual features](https://mlsanity.com/paper/1510.06479/) (2015)
- [PAC-Bayesian Theory Meets Bayesian Inference](https://mlsanity.com/paper/1605.08636/) (2016)
- [General Bayesian Loss Function Selection and the use of Improper Models](https://mlsanity.com/paper/2106.01214/) (2021)
- [Restricted Minimum Error Entropy Criterion for Robust Classification](https://mlsanity.com/paper/1909.02707/) (2019)
- [Direct Gibbs posterior inference on risk minimizers: construction, concentration, and calibration](https://mlsanity.com/paper/2203.09381/) (2022)
- [Quantized Minimum Error Entropy Criterion](https://mlsanity.com/paper/1710.04089/) (2017)
- [Correntropy-Based Logistic Regression with Automatic Relevance Determination for Robust Sparse Brain Activity Decoding](https://mlsanity.com/paper/2207.09693/) (2022)
- [Broad Learning System Based on Maximum Correntropy Criterion](https://mlsanity.com/paper/1912.11368/) (2019)
- [Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals](https://mlsanity.com/paper/2106.13086/) (2021)
- [Adaptive sparseness for correntropy-based robust regression via automatic relevance determination](https://mlsanity.com/paper/2302.00082/) (2023)
- [Sparse Bayesian Correntropy Learning for Robust Muscle Activity Reconstruction from Noisy Brain Recordings](https://mlsanity.com/paper/2404.15309/) (2024)
- [Correntropy-Based Improper Likelihood Model for Robust Electrophysiological Source Imaging](https://mlsanity.com/paper/2408.14843/) (2024)

## Abstract (arXiv)

Objective: Sparse Bayesian learning provides an effective framework to solve high-dimensional problems in brain signal decoding. However, conventional likelihoods regarding data distributions, such as Gaussian or Bernoulli, are potentially inadequate for handling the noisy recordings of brain activity. Hence, this work aims to formulate a robust sparse Bayesian learning framework to address noisy high-dimensional brain activity decoding. Methods: Motivated by the commendable robustness of the minimum error entropy learning criterion for addressing non-Gaussian signals, this study reformulated the sparse Bayesian learning framework under a generalized Bayesian paradigm, in which the model parameter is regulated with the minimum error entropy loss rather than a conventional likelihood function. Results: Our developed SBL-MEE algorithm was evaluated with two real-world brain decoding tasks of regression and classification scenarios, respectively. Experimental results demonstrated that our approach not only realizes superior brain decoding performance than existing methods, but also presents more physiologically interpretable decoder patterns. Conclusion: Although minimum error entropy is not constructed from an arbitrary probabilistic distribution, it is effective to establish noise-robust inference in sparse Bayesian learning method. Significance: This work provides a powerful tool to improve brain activity decoding capability, particularly regarding the noisy high-dimensional setting, thus promoting biomedical engineering applications such as brain-computer interface.