Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding
| Autor: | Jas, Mainak, Dupré La Tour, Tom, Şimşekli, Umut, Gramfort, Alexandre |
|---|---|
| Přispěvatelé: | Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), ANR-14-NEUC-0002,THALAMEEG,Revealing thalamocortical interactions in humans with integrated MEG/EEG, human intracranial recordings and computational neural modeling(2014), ANR-16-CE23-0014,FBIMATRIX,Méthodes distribuées et parallèles de Monte-Carlo par chaînes de Markov pour l'Inférence Bayésienne de modèles à factorisation de tenseurs(2016), European Project: 676943,H2020,SLAB(2016), Service NEUROSPIN (NEUROSPIN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria) |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Local field potentials Alpha stable probability distribution Convolutional sparse coding Machine Learning (stat.ML) Dictionary learning Statistics - Applications [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] Statistics - Machine Learning FOS: Biological sciences Quantitative Biology - Neurons and Cognition Heavy tailed distributions Neurons and Cognition (q-bio.NC) Applications (stat.AP) [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC] |
| Zdroj: | Advances in neural information processing systems Advances in neural information processing systems, Dec 2017, Long Beach, United States |
| Popis: | International audience; Neural time-series data contain a wide variety of prototypical signal waveforms (atoms) that are of significant importance in clinical and cognitive research. One of the goals for analyzing such data is hence to extract such 'shift-invariant' atoms. Even though some success has been reported with existing algorithms, they are limited in applicability due to their heuristic nature. Moreover, they are often vulnerable to artifacts and impulsive noise, which are typically present in raw neural recordings. In this study, we address these issues and propose a novel probabilistic convolutional sparse coding (CSC) model for learning shift-invariant atoms from raw neural signals containing potentially severe artifacts. In the core of our model, which we call αCSC, lies a family of heavy-tailed distributions called α-stable distributions. We develop a novel, computationally efficient Monte Carlo expectation-maximization algorithm for inference. The maximization step boils down to a weighted CSC problem, for which we develop a computationally efficient optimization algorithm. Our results show that the proposed algorithm achieves state-of-the-art convergence speeds. Besides, αCSC is significantly more robust to artifacts when compared to three competing algorithms: it can extract spike bursts, oscillations, and even reveal more subtle phenomena such as cross-frequency coupling when applied to noisy neural time series. |
| Databáze: | OpenAIRE |
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