Correntropy-Based Improper Likelihood Model for Robust Electrophysiological Source Imaging
<|reference_start|>Correntropy-Based Improper Likelihood Model for Robust Electrophysiological Source Imaging: Bayesian learning provides a unified skeleton to solve the electrophysiological source imaging task. From this perspective, existing source imaging algorithms utilize the Gaussian assumption for the observation noise to build the likelihood function for Bayesian inference. However, the electromagnetic measurements of brain activity are usually affected by miscellaneous artifacts, leading to a potentially non-Gaussian distribution for the observation noise. Hence the conventional Gaussian likelihood model is a suboptimal choice for the real-world source imaging task. In this study, we aim to solve this problem by proposing a new likelihood model which is robust with respect to non-Gaussian noises. Motivated by the robust maximum correntropy criterion, we propose a new improper distribution model concerning the noise assumption. This new noise distribution is leveraged to structure a robust likelihood function and integrated with hierarchical prior distributions to estimate source activities by variational inference. In particular, the score matching is adopted to determine the hyperparameters for the improper likelihood model. A comprehensive performance evaluation is performed to compare the proposed noise assumption to the conventional Gaussian model. Simulation results show that, the proposed method can realize more precise source reconstruction by designing known ground-truth. The real-world dataset also demonstrates the superiority of our new method with the visual perception task. This study provides a new backbone for Bayesian source imaging, which would facilitate its application using real-world noisy brain signal.<|reference_end|>
@article{li2024correntropy-based,
    title={Correntropy-Based Improper Likelihood Model for Robust
  Electrophysiological Source Imaging},
    author={Yuanhao Li, Badong Chen, Zhongxu Hu, Keita Suzuki, Wenjun Bai,
  Yasuharu Koike, Okito Yamashita},
    journal={arXiv preprint arXiv:2408.14843},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14843},
    primaryClass={cs.LG cs.NE eess.SP}
}