MultiView Independent Component Analysis with Delays

• URL: http://arxiv.org/abs/2312.00484v1
• Date: Fri, 1 Dec 2023 10:33:16 GMT
• Title: MultiView Independent Component Analysis with Delays
• Authors: Ambroise Heurtebise, Pierre Ablin, Alexandre Gramfort
• Abstract summary: We present MultiView Independent Component Analysis with Delays (MVICAD) MVICAD builds on the MultiView ICA model by allowing sources to be delayed versions of some shared sources. As ICA is often used in neuroscience, we show that latencies are age-related when applied to Cam-CAN, a large-scale magnetoencephalography (MEG) dataset.
• Score: 72.19163346293848
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Linear Independent Component Analysis (ICA) is a blind source separation technique that has been used in various domains to identify independent latent sources from observed signals. In order to obtain a higher signal-to-noise ratio, the presence of multiple views of the same sources can be used. In this work, we present MultiView Independent Component Analysis with Delays (MVICAD). This algorithm builds on the MultiView ICA model by allowing sources to be delayed versions of some shared sources: sources are shared across views up to some unknown latencies that are view- and source-specific. Using simulations, we demonstrate that MVICAD leads to better unmixing of the sources. Moreover, as ICA is often used in neuroscience, we show that latencies are age-related when applied to Cam-CAN, a large-scale magnetoencephalography (MEG) dataset. These results demonstrate that the MVICAD model can reveal rich effects on neural signals without human supervision.

Related papers

• Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis [55.03872260158717]
  Resting-state MRI functional (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis. Many methods have been proposed to reduce fMRI heterogeneity between source and target domains. But acquiring source data is challenging due to concerns and/or data storage burdens in multi-site studies. We design a source-free collaborative domain adaptation framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible.
  arXiv  Detail & Related papers  (2023-08-24T01:30:18Z)
• On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM Signals [104.11663769306566]
  We study the single-channel source separation problem involving frequency-division multiplexing (OFDM) signals. We propose critical domain-informed modifications to the network parameterization, based on insights from OFDM structures.
  arXiv  Detail & Related papers  (2023-03-11T16:29:13Z)
• Multi-View Independent Component Analysis with Shared and Individual Sources [0.0]
  Independent component analysis (ICA) is a blind source separation method for linear disentanglement of independent latent sources from observed data. We prove that the corresponding linear structure is identifiable, and the shared sources can be recovered, provided that sufficiently many diverse views and data points are available. We show empirically that our objective recovers the sources in high dimensional settings, also in the case when the measurements are corrupted by noise.
  arXiv  Detail & Related papers  (2022-10-05T08:23:05Z)
• Spatio-temporally separable non-linear latent factor learning: an application to somatomotor cortex fMRI data [0.0]
  Models of fMRI data that can perform whole-brain discovery of latent factors are understudied. New methods for efficient spatial weight-sharing are critical to deal with the high dimensionality of the data and the presence of noise. Our approach is evaluated on data with multiple motor sub-tasks to assess whether the model captures disentangled latent factors that correspond to each sub-task.
  arXiv  Detail & Related papers  (2022-05-26T21:30:22Z)
• Shared Independent Component Analysis for Multi-Subject Neuroimaging [107.29179765643042]
  We introduce Shared Independent Component Analysis (ShICA) that models each view as a linear transform of shared independent components contaminated by additive Gaussian noise. We show that this model is identifiable if the components are either non-Gaussian or have enough diversity in noise variances. We provide empirical evidence on fMRI and MEG datasets that ShICA yields more accurate estimation of the components than alternatives.
  arXiv  Detail & Related papers  (2021-10-26T08:54:41Z)
• One to Multiple Mapping Dual Learning: Learning Multiple Sources from One Mixed Signal [38.00036571066844]
  Single channel blind source separation (SCBSS) refers to separate multiple sources from a mixed signal collected by a single sensor. An algorithm is proposed in this paper to separate multiple sources from a mixture by designing a parallel dual generative adversarial Network (PDualGAN) This algorithm can be applied to any mixed model such as linear instantaneous mixed model and convolutional mixed model.
  arXiv  Detail & Related papers  (2021-10-13T08:34:02Z)
• Adaptive Multi-View ICA: Estimation of noise levels for optimal inference [65.94843987207445]
  Adaptive multiView ICA (AVICA) is a noisy ICA model where each view is a linear mixture of shared independent sources with additive noise on the sources. On synthetic data, AVICA yields better sources estimates than other group ICA methods thanks to its explicit MMSE estimator. On real magnetoencephalograpy (MEG) data, we provide evidence that the decomposition is less sensitive to sampling noise and that the noise variance estimates are biologically plausible.
  arXiv  Detail & Related papers  (2021-02-22T13:10:12Z)
• Modeling Shared Responses in Neuroimaging Studies through MultiView ICA [94.31804763196116]
  Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. We propose a novel MultiView Independent Component Analysis model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects.
  arXiv  Detail & Related papers  (2020-06-11T17:29:53Z)
• Source Separation with Deep Generative Priors [17.665938343060112]
  We use generative models as priors over the components of a mixture of sources, and noise-annealed Langevin dynamics to sample from the posterior distribution of sources given a mixture. This decouples the source separation problem from generative modeling, enabling us to directly use cutting-edge generative models as priors. The method achieves state-of-the-art performance for MNIST digit separation.
  arXiv  Detail & Related papers  (2020-02-19T00:48:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.