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1. Research of Low-Rank Representation and Discriminant Correlation Analysis for Alzheimer's Disease Diagnosis.

Author

Li Z, Dong A, and Zhou J

Subjects
Artificial Intelligence, Case-Control Studies, Cognitive Dysfunction diagnosis, Cognitive Dysfunction diagnostic imaging, Computational Biology, Diagnosis, Computer-Assisted statistics & numerical data, Discriminant Analysis, Humans, Magnetic Resonance Imaging statistics & numerical data, Multimodal Imaging statistics & numerical data, Neuroimaging statistics & numerical data, Positron-Emission Tomography statistics & numerical data, Support Vector Machine, Algorithms, Alzheimer Disease diagnosis, Alzheimer Disease diagnostic imaging, Diagnosis, Computer-Assisted methods
Abstract

As population aging is becoming more common worldwide, applying artificial intelligence into the diagnosis of Alzheimer's disease (AD) is critical to improve the diagnostic level in recent years. In early diagnosis of AD, the fusion of complementary information contained in multimodality data (e.g., magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF)) has obtained enormous achievement. Detecting Alzheimer's disease using multimodality data has two difficulties: (1) there exists noise information in multimodal data; (2) how to establish an effective mathematical model of the relationship between multimodal data? To this end, we proposed a method named LDF which is based on the combination of low-rank representation and discriminant correlation analysis (DCA) to fuse multimodal datasets. Specifically, the low-rank representation method is used to extract the latent features of the submodal data, so the noise information in the submodal data is removed. Then, discriminant correlation analysis is used to fuse the submodal data, so the complementary information can be fully utilized. The experimental results indicate the effectiveness of this method., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2020 Zhigang Li et al.)

Published
2020
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2. Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)

Author

Xavier Rossello, Jannick AN Dorresteijn, Arne Janssen, Ekaterini Lambrinou, Martijn Scherrenberg, Eric Bonnefoy-Cudraz, Mark Cobain, Massimo F Piepoli, Frank LJ Visseren, Paul Dendale, null This paper is a co-publication betw, Centro Nacional de Investigaciones Cardiovasculares Carlos III [Madrid, Spain] (CNIC), Instituto de Salud Carlos III [Madrid] (ISC), Centro de Investigación Biomédica en Red en Enfermedades Cardiovasculares (CIBERCV), University Medical Center [Utrecht], Jessa Ziekenhuis [Hasselt], Cyprus University of Technology, Hasselt University (UHasselt), Cardiovasculaire, métabolisme, diabétologie et nutrition (CarMeN), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Hospices Civils de Lyon (HCL), Service de Cardiologie Lyon (Hôpital Louis Pradel [CHU - HCL]), Hôpital Louis Pradel [CHU - HCL], Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), Imperial College London, University of Southern California (USC), Rossello, Xavier/0000-0001-6783-8463, Rossello, Xavier, Dorresteijn, Jannick A. N., JANSSEN, Arne, Lambrinou, Ekaterini, SCHERRENBERG, Martijn, Bonnefoy-Cudraz, Eric, Cobain, Mark, Piepoli, Massimo F., Visseren, Frank L. J., DENDALE, Paul, CarMeN, laboratoire, Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
Male, Time Factors, Epidemiology, [SDV]Life Sciences [q-bio], Allied Health Personnel, Disease, 030204 cardiovascular system & hematology, Critical Care and Intensive Care Medicine, Medical and Health Sciences, 0302 clinical medicine, prevention, Risk Factors, cardiovascular disease, Preventive Health Services, Advanced and Specialised Nursing, 030212 general & internal medicine, Cardiovascular nursing, Societies, Medical, Aged, 80 and over, biology, risk assessment, General Medicine, Middle Aged, Prognosis, Risk prediction, 3. Good health, [SDV] Life Sciences [q-bio], Preventive cardiology, Europe, Primary Prevention, Medical–Surgical Nursing, Cardiovascular Diseases, Practice Guidelines as Topic, Female, patient, Cardiology and Cardiovascular Medicine, Risk assessment, Algorithms, Adult, Cardiovascular Nursing, medicine.medical_specialty, Critical Care, Clinical Decision-Making, Cardiology, Decision Support Techniques, 03 medical and health sciences, Predictive Value of Tests, Health Sciences, Medical–Surgical, medicine, Humans, Medical history, Intensive care medicine, Association (psychology), Aged, Advanced and Specialized Nursing, patient Keywords Risk prediction, Acca, Models, Statistical, business.industry, biology.organism_classification, Lifetime risk, business, Forecasting
Abstract

Risk assessment and risk prediction have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of - usually interactive and online available - tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: this paper was produced within the framework of the ESC Prevention of Cardiovascular Disease Programme which is led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP). The ESC Prevention of Cardiovascular Disease Programme is supported by unrestricted educational grants. The authors received no financial support for the research, authorship, and/or publication of this article. Rossello, X (corresponding author), Ctr Nacl Invest Cardiovasc CNIC Carlos III, Melchor Fernandez Almagro 3, Madrid 28029, Spain. fjrossello@cnic.es

Published
2020

3. Computing Riemann-Roch spaces via Puiseux expansions

Author

Simon Abelard, Elena Berardini, Alain Couvreur, Grégoire Lecerf, Thales SIX GTS France, Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X), Geometry, arithmetic, algorithms, codes and encryption (GRACE), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-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), This paper is part of a project of École polytechnique, that has received funding from the French 'Agence de l'innovation de défense'. Simon Abelard was partially funded by this grant, when he was hosted at École polytechnique, Institut Polytechnique de Paris (91120 Palaiseau, France), from October 2019 to the end of December 2020., École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), and This paper is part of a project of École polytechnique, that has received funding from the French 'Agence de l'innovation de défense'. Simon Abelard was partially funded by this grant, when he was hosted at École polytechnique, Institut Polytechnique de Paris (91120 Palaiseau, France), from October 2019 to the end of December 2020. Elena Berardini has also received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 899987.

Subjects
Statistics and Probability, [INFO.INFO-SC]Computer Science [cs]/Symbolic Computation [cs.SC], Numerical Analysis, Control and Optimization, Algebra and Number Theory, Applied Mathematics, General Mathematics, Algebraic curves, Complexity, Riemann-Roch spaces, ComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION, [MATH.MATH-AG]Mathematics [math]/Algebraic Geometry [math.AG], Puiseux expansions, Algorithms
Abstract

International audience; Computing large Riemann-Roch spaces for plane projective curves still constitutes a major algorithmic and practical challenge. Seminal applications concern the construction of arbitrarily large algebraic geometry error correcting codes over alphabets with bounded cardinality. Nowadays such codes are increasingly involved in new areas of computer science such as cryptographic protocols and "interactive oracle proofs". In this paper, we design a new probabilistic algorithm of Las Vegas type for computing Riemann-Roch spaces of smooth divisors, in characteristic zero, and with expected complexity exponent 2.373 (a feasible exponent for linear algebra) in terms of the input size.

Published
2021

4. Optimizing Motor Intention Detection With Deep Learning: Towards Management of Intraoperative Awareness

Author

Laurent Bougrain, Oleksii Avilov, Anton Popov, Sébastien Rimbert, National Technical University of Ukraine 'Kyiv Polytechnic Institute' [Kiev], Analysis and modeling of neural systems by a system neuroscience approach (NEUROSYS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Oleksii Avilov was supported by scholarship from the French Embassy to Ukraine while working on this topic at the NEUROSYS team at LORIA (Université de Lorraine/CNRS/Inria), Nancy, France. Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (https://www.grid5000.fr)., Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0209 industrial biotechnology, Computer science, Biomedical Engineering, motor imagery AAGA: accidental awareness during general anesthesia, 02 engineering and technology, Intention, Electroencephalography, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Intraoperative Awareness, 020901 industrial engineering & automation, Motor imagery, median nerve stimulation, Deep Learning, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], intraoperative awareness during general anesthesia, 0202 electrical engineering, electronic engineering, information engineering, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Functional electrical stimulation, Humans, Brain-computer interface (BCI), Artificial neural network, medicine.diagnostic_test, Median nerve stimulation, business.industry, electroencephalogram (EEG), Deep learning, [SCCO.NEUR]Cognitive science/Neuroscience, Pattern recognition, Linear discriminant analysis, Median nerve, medicine.anatomical_structure, machine learning, Frontal lobe, Brain-Computer Interfaces, Imagination, 020201 artificial intelligence & image processing, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms, Motor cortex
Abstract

International audience; Objective: This article shows the interest in deep learning techniques to detect motor imagery (MI) from raw electroencephalographic (EEG) signals when a functional electrical stimulation is added or not. Impacts of electrode montages and bandwidth are also reported. The perspective of this work is to improve the detection of intraoperative awareness during general anesthesia. Methods: Various architectures of EEGNet were investigated to optimize MI detection. They have been compared to the state-of-the-art classifiers in Brain-Computer Interfaces (based on Riemannian geometry, linear discriminant analysis), and other deep learning architectures (deep convolution network, shallow convolutional network). EEG data were measured from 22 participants performing motor imagery with and without median nerve stimulation. Results: The proposed architecture of EEGNet reaches the best classification accuracy (83.2%) and false-positive rate (FPR 19.0%) for a setup with only six electrodes over the motor cortex and frontal lobe and for an extended 4-38 Hz EEG frequency range while the subject is being stimulated via a median nerve. Configurations with a larger number of electrodes result in higher accuracy (94.5%) and FPR (6.1%) for 128 electrodes (and respectively 88.0% and 12.9% for 13 electrodes).Conclusion: The present work demonstrates that using an extended EEG frequency band and a modified EEGNet deep neural network increases the accuracy of MI detection when used with as few as 6 electrodes which include frontal channels. Significance: The proposed method contributes to the development of Brain-Computer Interface systems based on MI detection from EEG.

Published
2021

5. Incorporating individual historical controls and aggregate treatment effect estimates into a Bayesian survival trial: a simulation study

Author

Lisa V. Hampson, Marie-Cécile Le Deley, Caroline Brard, Nathalie Gaspar, Gwénaël Le Teuff, Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Paris-Sud - Paris 11 (UP11)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de biostatistique et d'épidémiologie (SBE), Direction de la recherche clinique [Gustave Roussy], Institut Gustave Roussy (IGR)-Institut Gustave Roussy (IGR), Statistical Methodology [Bâle, Suisse], Novartis Pharma AG, Département de cancérologie de l'enfant et de l'adolescent [Gustave Roussy], Institut Gustave Roussy (IGR), Unité de Méthodologie et de Biostatistique [Lille] (UMB), Centre Régional de Lutte contre le Cancer Oscar Lambret [Lille] (UNICANCER/Lille), Université de Lille-UNICANCER-Université de Lille-UNICANCER, We acknowledge for the design, analysis, interpretation of the simulation study, and for the writing of the manuscript, funding from – PhD grant from doctoral school of public health, Paris-Sud, Paris-Saclay – Lisa Hampson’s contribution to the paper was partly funded by the UK Medical Research Council (grant MR/M013510/1) and was completed while she was an employee of Lancaster University – Ligue contre le cancer, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris-Sud - Paris 11 (UP11)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Université Lille Nord de France (COMUE)-UNICANCER-Université Lille Nord de France (COMUE)-UNICANCER, and Bodescot, Myriam

Subjects
Simulation study, Epidemiology, Computer science, Bayesian probability, Health Informatics, [SDV.CAN]Life Sciences [q-bio]/Cancer, Power prior, law.invention, 03 medical and health sciences, 0302 clinical medicine, Adjuvants, Immunologic, [SDV.CAN] Life Sciences [q-bio]/Cancer, Randomized controlled trial, law, Statistics, Prior probability, Humans, Computer Simulation, 030212 general & internal medicine, Set (psychology), Baseline (configuration management), Bayesian randomised survival trial, Randomized Controlled Trials as Topic, Weibull distribution, Osteosarcoma, lcsh:R5-920, Phosphatidylethanolamines, 030503 health policy & services, Aggregate (data warehouse), Bayes Theorem, Variance (accounting), Models, Theoretical, [SDV.SP]Life Sciences [q-bio]/Pharmaceutical sciences, Control Groups, Mixture prior, 3. Good health, [SDV.SP] Life Sciences [q-bio]/Pharmaceutical sciences, Research Design, Sample Size, Individual control data, 0305 other medical science, lcsh:Medicine (General), Aggregate treatment effect, Acetylmuramyl-Alanyl-Isoglutamine, Rare disease, Algorithms, Research Article
Abstract

Background Performing well-powered randomised controlled trials (RCTs) of new treatments for rare diseases is often infeasible. However, with the increasing availability of historical data, incorporating existing information into trials with small sample sizes is appealing in order to increase the power. Bayesian approaches enable one to incorporate historical data into a trial’s analysis through a prior distribution. Methods Motivated by a RCT intended to evaluate the impact on event-free survival of mifamurtide in patients with osteosarcoma, we performed a simulation study to evaluate the impact on trial operating characteristics of incorporating historical individual control data and aggregate treatment effect estimates. We used power priors derived from historical individual control data for baseline parameters of Weibull and piecewise exponential models, while we used a mixture prior to summarise aggregate information obtained on the relative treatment effect. The impact of prior-data conflicts, both with respect to the parameters and survival models, was evaluated for a set of pre-specified weights assigned to the historical information in the prior distributions. Results The operating characteristics varied according to the weights assigned to each source of historical information, the variance of the informative and vague component of the mixture prior and the level of commensurability between the historical and new data. When historical and new controls follow different survival distributions, we did not observe any advantage of choosing a piecewise exponential model compared to a Weibull model for the new trial analysis. However, we think that it remains appealing given the uncertainty that will often surround the shape of the survival distribution of the new data. Conclusion In the setting of Sarcome-13 trial, and other similar studies in rare diseases, the gains in power and accuracy made possible by incorporating different types of historical information commensurate with the new trial data have to be balanced against the risk of biased estimates and a possible loss in power if data are not commensurate. The weights allocated to the historical data have to be carefully chosen based on this trade-off. Further simulation studies investigating methods for incorporating historical data are required to generalise the findings. Electronic supplementary material The online version of this article (10.1186/s12874-019-0714-z) contains supplementary material, which is available to authorized users.

Published
2019

6. Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations

Author

Michèle Sebag, Cédric Gouy-Pailler, Yoann Isaac, Quentin Barthélemy, Jamal Atif, Laboratoire d'analyse des données et d'intelligence des systèmes (LADIS), Département Métrologie Instrumentation & Information (DM2I), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, TAckling the Underspecified (TAU), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-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), Mensia Technologies [Rennes], Mensia Technologies [Paris], Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), The work presented in this paper has been partially funded by DIGITEO under the Grant 2011-053D, Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, and CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)

Subjects
FOS: Computer and information sciences, Signal processing, Optimization problem, Noise reduction, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, Machine Learning (cs.LG), [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Recovery, Computer Science - Data Structures and Algorithms, Prior probability, Regularization, 0202 electrical engineering, electronic engineering, information engineering, Data Structures and Algorithms (cs.DS), Electrical and Electronic Engineering, Selection, Image restoration, Mathematics, business.industry, Linear Inverse Problems, 020206 networking & telecommunications, Pattern recognition, Sparse approximation, [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA], Regression, Minimization, Computer Science - Learning, Image-Restoration, Control and Systems Engineering, Statistical analysis, 020201 artificial intelligence & image processing, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Computer Vision and Pattern Recognition, Decomposition method (constraint satisfaction), Artificial intelligence, Minification, Lasso, business, Software, Algorithms, [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
Abstract

This paper addresses the structurally constrained sparse decomposition of multi-dimensional signals onto overcomplete families of vectors, called dictionaries. The contribution of the paper is threefold. Firstly, a generic spatio-temporal regularization term is designed and used together with the standard ź 1 regularization term to enforce a sparse decomposition preserving the spatio-temporal structure of the signal. Secondly, an optimization algorithm based on the split Bregman approach is proposed to handle the associated optimization problem, and its convergence is analyzed. Our well-founded approach yields same accuracy as the other algorithms at the state of the art, with significant gains in terms of convergence speed. Thirdly, the empirical validation of the approach on artificial and real-world problems demonstrates the generality and effectiveness of the method. On artificial problems, the proposed regularization subsumes the Total Variation minimization and recovers the expected decomposition. On the real-world problem of electro-encephalography brainwave decomposition, the approach outperforms similar approaches in terms of P300 evoked potentials detection, using structured spatial priors to guide the decomposition. HighlightsA sparse structured decomposition method is proposed for multi-dimensional signals.Knowledge priors are encoded in a regularization to obtain plausible representations.The proposed split-Bregman based method outperforms counterparts in terms of speed.The approach is applied to EEG denoising for the extraction of P300 potentials.

Published
2017