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138 results on '"Ranta, Radu"'

1. Evaluation of Performance for Human In-Vivo Conductivity Estimation from EEG and sEEG Recorded in Simultaneous with Intracerebral Electrical Stimulation

Author

Altakroury, Hamza, Koessler, Laurent, Ranta, Radu, Hofmanis, Janis, Coulbois, Sophie Colnat, Maillard, Louis, and Dorr, Valérie Louis

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

Assigning accurate conductivity values in human head models is an essential factor for performing precise electroencephalographic (EEG) source localization and targeting of transcranial electrical stimulation (TES). Unfortunately, the literature reports diverging conductivity values of the different tissues in the human head. The current study analyzes first the performance of in-vivo conductivity estimation for different configurations concerning the localization of the electrical source and measurement. Then, it presents conductivity estimates for three epileptic patients using scalp EEG and intracerebral stereotactic EEG (sEEG) acquired in simultaneous with intracerebral electrical stimulation. The estimates of the conductivities were based on finite-element models of the human head with five tissue compartments of homogeneous and isotropic conductivities. The results of this study show that in-vivo conductivity estimation can lead to different estimated conductivities for the same patient when considering different stimulation positions, different measurement positions or different measurement modalities (sEEG or EEG). This work provides important guidelines to in-vivo conductivity estimation and explains the variability among the conductivity values which have been reported in previous studies.

Published
2022

6. Convergence and performances of the peeling wavelet denoising algorithm

Author

Lacaux, Céline, Muller, Aurélie, Ranta, Radu, and Tindel, Samy

Subjects
Mathematics - Statistics Theory, 62G08,62G20
Abstract

This note is devoted to an analysis of the so-called peeling algorithm in wavelet denoising. Assuming that the wavelet coefficients of the signal can be modeled by generalized Gaussian random variables, we compute a critical thresholding constant for the algorithm, which depends on the shape parameter of the generalized Gaussian distribution. We also quantify the optimal number of steps which have to be performed, and analyze the convergence of the algorithm. Several versions of the obtained algorithm were implemented and tested against classical wavelet denoising procedures on benchmark and simulated biological signals., Comment: 20 pages

Published
2009

10. OLS-R1: estimation parcimonieuse de sources cérébrales par régression itérative sous contrainte de rang

Author

Ranta, Radu, Le Cam, Steven, and Ranta, Radu

Subjects
Sparse inverse problems, EEG / MEG source imaging, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

This paper introduces a new sparse algorithm from the iterative regression family. Unlike the classical OLS, the elements of the dictionary are not vectors but matrices. Moreover, the (vectorial) regression coefficients representing time varying amplitudes are constrained to unit rank. The target application is the inverse problem in brain source estimation. On simulated data, the proposed algorithm shows better performances than classical solutions used for solving the mentioned inverse problem., Cet article propose un nouvel algorithme parcimonieux par régression itératives sur dictionnaire. A la différence d'un OLS classique, les éléments du dictionnaire ne sont pas des vecteurs mais des matrices. Pour chaque élément, les coefficients (vectoriels) de la régression, qui représentent des amplitudes variables dans le temps, sont en plus contraints à être de rang unitaire. L'application visée est le problème inverse en électrophysiologie cérébrale (estimation de sources dipolaires). L'algorithme proposé, pour lequel on montre la convergence et la monotonie, se compare favorablement aux méthodes classiquement utilisées dans cette application.

Published
2022

11. Ocular Artifacts Elimination From Multivariate EEG Signal Using Frequency-Spatial Filtering

Author

Bhattacharyya, Abhijit, Verma, Aarushi, Ranta, Radu, and Pachori, Ram Bilas

Abstract

The electroencephalogram (EEG) signals record electrical activities generated by the brain cells and are used as a state-of-the-art diagnosis tool for various neural disorders. However, the unwanted artifacts often contaminate the recorded EEG signals and disturb the interpretation of the neuronal activity. This article aims to propose an efficient automatic method to eliminate the ocular artifacts (OAs) from the multichannel EEG signals with novel frequency-spatial filtering. The method combines dictionary-based spatial filtering and frequency-based signal decomposition method, namely, empirical wavelet transform (EWT). The artifact dictionary needed for spatial filtering is isolated from the raw data by: 1) selecting the contaminated channels and 2) frequency-domain filtering. More precisely, the $\delta $ -rhythms of identified highly contaminated channels are selected and placed into an artifact dictionary. Afterward, the $\delta $ -rhythms of multichannel EEG signals are spatially filtered using the built dictionary to seclude the OAs within a limited number of components. Furthermore, the artifact components are eliminated and clean $\delta $ -rhythms are recovered using the inverse spatial filtering technique. Finally, the clean $\delta $ -rhythms are combined with other EEG rhythms to reconstruct the OA-free signals. The proposed method is applied to OA-contaminated synthetic and real multichannel EEG signals with a convincing performance as compared to state-of-the-art approaches. The proposed method removes the OAs without affecting the background EEG information. The proposed method can ease sensor signal interpretation and further processing, e.g., for BCI applications.

Published
2023
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14. Design of experiments and Sobol' sensitivity analysis of a hippocampus computational model

Author

Aussel, Amélie, Buhry, Laure, Ranta, Radu, Ranta, Radu, Centre de Recherche en Automatique de Nancy (CRAN), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), 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), 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), 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), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)

Subjects
Neurons, hippocampus, 0206 medical engineering, 02 engineering and technology, Sleep, Slow-Wave, 020601 biomedical engineering, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Epilepsy, Temporal Lobe, design of experiment (DOE), Sobol' sensititvity analysis, Humans, parameter optimization, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, Wakefulness, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, computational neuroscience, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; The hippocampus is a brain area involved in many memory processes. This structure can also be affected in neurological diseases such as mesial temporal lobe epilepsy. A better understanding of its electrophysiological activity could benefit both the neuroscientific and clinical communities. We proposed, in a previous paper, a detailed bio-realistic conductance-based mathematical model of more than thirty thousand neurons to reproduce the main oscillatory features of the healthy hippocampus during slow-wave sleep and wakefulness, from slow to very fast frequencies. One big challenge of this model is its parametrization. The aim of the present work is to combine neuroscientific expertise and systematic yet efficient exploration of the highly dimensional parameter space using well defined identification methods, namely the design of experiments and the Sobol's sensitivity analysis.

Published
2021

24. Extracellular synaptic and action potential signatures in the hippocampal formation: a modelling study

Author

Aussel, Amélie, Tran, Harry, Buhry, Laure, Le Cam, Steven, Maillard, Louis, Colnat-Coulbois, Sophie, Louis-Dorr, Valérie, Ranta, Radu, Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Automatique de Nancy (CRAN), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), 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), 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), 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), Service de neurologie [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Service de Neurochirurgie [CHRU Nancy], Maquin, Didier, and Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SPI.AUTO] Engineering Sciences [physics]/Automatic, ComputingMilieux_MISCELLANEOUS, [SPI.AUTO]Engineering Sciences [physics]/Automatic
Abstract

International audience

Published
2019

25. Extracellular recordings simulation: a time-efficient method to model action potentials signatures of neuronal populations

Author

Tran, Harry, Ranta, Radu, Le Cam, Steven, Louis-Dorr, Valérie, Centre de Recherche en Automatique de Nancy (CRAN), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), and Tran, Harry

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, [SDV.NEU.NB] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, ComputingMethodologies_GENERAL, [SDV.NEU.SC] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, ComputingMilieux_MISCELLANEOUS
Abstract

Présentation Poster; International audience

Published
2019

26. Development of a biometric intrabody communication

Author

Huët, Romain, Levionnais, Philippe, Lepetit, Olivier, Ranta, Radu, Louis-Dorr, Valérie, Maquin, Didier, Centre de Recherche en Automatique de Nancy (CRAN), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), France Télécom Recherche & Développement (FT R&D), and France Télécom

Subjects
biometrics, [SPI.AUTO] Engineering Sciences [physics]/Automatic, intrabody communication, NFC emitter, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.AUTO]Engineering Sciences [physics]/Automatic, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; This paper presents the development and the evaluation of a biometric device using IntraBody Communication (IBC). The receiver is presented as a mobile phone casing and can be plugged into a smartphone. The emitter is a fixed antenna whose distance to the user hand may vary in time, depending of the user’s movements. The main objective of this device’s development is to evaluate whether the gesture of approach and contact the antenna is an inter-individual signature. A comparison is made between conventional IBC devices and our prototype (called Orange prototype). A preliminary study for evaluating the feasibility of biometric identification using the proposed device is also developed in this paper.

Published
2019

28. Transformaciones laplacianas como técnica de localización de regiones de actividad cortical en sujetos sordos

Author

Romo-Vázquez, Rebeca, Romo-Flores, Juan, Ranta, Radu, González-Garrido, Andrés, Vélez-Pérez, Hugo Abraham, Ruiz-Stovel, Vanessa, Maquin, Didier, Universidad Jesuita de Guadalajara (ITESO), Centre de Recherche en Automatique de Nancy (CRAN), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), and Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SPI.AUTO] Engineering Sciences [physics]/Automatic, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.AUTO]Engineering Sciences [physics]/Automatic, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

Published in Memorias del Congreso Nacional de Ingeniería Biomédica, 5(1):118-121, oct. 2018. ISSN 2395-8928; National audience; La discriminación vibrotáctil representa una alternativa viable para entrenar pacientes con sordera congénita bilateral profunda en la adquisición del lenguaje. Sin embargo, se conoce poco sobre las modificaciones en la dinámica temporal de los sustratos neurales involucrados en este entrenamiento específico, en parte debido a las limitaciones de los métodos tradicionales de análisis de la actividad eléctrica cerebral (EEG).En el presente trabajo se presenta la implementación de una metodología para la localización de cambios en la actividad cerebral a nivel cortical basada en el análisis del EEG, empleando operadores laplacianos para obtener reconstrucciones espacio-temporales en modelos cefálicos de geometría realista y poder comparar el efecto del entrenamiento en discriminación vibrotáctil en sordos profundos prelinguales y controles normoyentes, mientras realizaban una tarea de discriminación por esta vía sensorial de tonos puros.A partir de los operadores y modelos utilizados se demostró un efecto distintivo del entrenamiento sobre la activación de zonas temporo-parietales específicas en sujetos sordos, que concuerda con hallazgos previos usando métodos en imágenes y que se han asociado al procesamiento de características de estímulos auditivos. Este resultado sugiere interesantes perspectivas prácticas para el estudio de la dinámica neurofuncional temporal del aprendizaje discriminativo vibrotáctil en este tipo de pacientes.

Published
2018

33. SEEG brain source imaging

Author

Le Cam, Steven, Ranta, Radu, Caune, Vairis, Koessler, Laurent, Maillard, Louis, Louis-Dorr, Valérie, Centre de Recherche en Automatique de Nancy (CRAN), and Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
Abstract

published in Clinical EEG and Neuroscience 49(6):NP1-NP46, November 2018; International audience

Published
2017

35. Oscillation analysis of ring networks : application on epileptic hippocampus

Author

Aussel, Amélie, Ranta, Radu, Centre de Recherche en Automatique de Nancy (CRAN), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), 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), 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), 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), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)

Subjects
Computational Neuroscience, Bernstein Conference, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [SPI.AUTO]Engineering Sciences [physics]/Automatic
Abstract

Poster; International audience; Normal neural population activity and information encoding in the hippocampus can be modeled with ring attractors dynamics[1], but this representation has not yet been linked to pathological hippocampus activity such as temporal lobe epileptic seizures. In this study, we wanted to investigate under which conditions these dynamic models can develop oscillatory activities characteristic of the temporal lobe epilepsy. In particular,we focused on the influence of the synaptic connectivity[2][3]. We performed the analysis of networks consisting in one or two rings of excitatory or inhibitory rate model neurons with piece-wise linear activation functions and spatially symmetric synaptic connections. We evaluated their stability under constant input current considering either the role of the real/complex eigenvalues of the system or the role of the switches dues to the nonlinearity of its activation functions. Our main results are that : (1) single-ring networks cannot oscillate, neither due to their eigenvalues nor due the nonlinearity of their activation functions, and (2) two-ring networks have more complex dynamics that enable them to exhibit either normal or pathological activity, depending on their connectivity. In particular, switches proveoked by the nonllinear activation functions can cause these systems to present a limit cycle. We then verified our predictions using network simulations, for different synaptic connection range and different input levels. Inthe case of two-ring networks, we observed that networks with only short-range synaptic connections were stable for all inputs, thus describing healthy hippocampus activity, whereas networks with unbalanced connections could oscillate for certain input levels, while remaining stable for others, thus representing plausible epileptic networks.

Published
2016

39. A coarse discretization of the brain volume for solving the inverse problem

Author

Tran, Harry, Le Cam, Steven, Ranta, Radu, Centre de Recherche en Automatique de Nancy (CRAN), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), and Tran, Harry

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, [SDV.NEU.NB] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, [SDV.NEU.SC] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2015

40. Intracerebral Stimulation. Role of the supplementary motor area (SMA)

Author

Salido-Ruiz, Ricardo-Antonio, Ranta, Radu, Koessler, Laurent, Louis-Dorr, Valérie, Maillard, Louis, Centre de Recherche en Automatique de Nancy (CRAN), Université Henri Poincaré - Nancy 1 (UHP)-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Service de neurologie [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), and Ranta, Radu

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, EEG, stimulation, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Published
2010

41. Relationships estimation of scalp EEG after preprocessing

Author

Velez-Perez, Hugo, Romo-Vázquez, Rebeca, Ranta, Radu, Louis-Dorr, Valérie, Maillard, Louis, Centre de Recherche en Automatique de Nancy (CRAN), Université Henri Poincaré - Nancy 1 (UHP)-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Service de neurologie [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), and Ranta, Radu

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, connectivity, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, EEG, preprocessing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SDV.NEU.SC] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Published
2010

42. Reference estimation using blind source separation

Author

Ranta, Radu, Salido-Ruiz, Ricardo-Antonio, Louis-Dorr, Valérie, Koessler, Laurent, Centre de Recherche en Automatique de Nancy (CRAN), Université Henri Poincaré - Nancy 1 (UHP)-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Service de neurologie [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), and Ranta, Radu

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, reference estimation, EEG, BSS, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

Research background and aims It is well known that real EEGs, as any other electrophysiological activity, are measured relatively to a non-null reference signal. Therefore, the electrical activity recorded by the reference electrode influences all the other measures and might disturb the interpretation of the signals, as well as their automatic analysis (synchronicity evaluation for example). The goal of this communication is to correct the recorded EEGs by estimating the unknown reference signal. Two recording models are possible, depending on the position of the reference electrode, far or close to the recording site. This communication focuses on model I (non-null reference independent from the interest signals), although model II will be briefly exposed. Methods A similar problem was addressed by Hu (2007), the proposed solution being based on an ICA algorithm. We propose here a new method, faster and more accurate, also based on an ICA model. During a first step, a normalized reference is estimated using the particular structure of the mixing matrix in the case of a non-null referenced model. Second, the amplitude of the reference is estimated by minimizing a mean squared error (MSE) type criterion. Results and conclusions Several simulations are used to illustrate the proposed method. Using the MSE between the estimated and the simulated reference signal, we show that our method outperforms previously proposed methods for all noise levels. Coherence estimations for the simulated signals are also presented, before and after reference correction. An example on a real depth EEG signal concludes the communication.

Published
2010

43. Analysis of 5 source separation algorithms on simulated EEG signals

Author

Salido-Ruiz, Ricardo-Antonio, Romo-Vázquez, Rebeca, Ranta, Radu, Leija, Lorenzo, Ranta, Radu, Centro de Investigacion y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV), Centre de Recherche en Automatique de Nancy (CRAN), and Université Henri Poincaré - Nancy 1 (UHP)-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDV.IB] Life Sciences [q-bio]/Bioengineering, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [SDV.IB]Life Sciences [q-bio]/Bioengineering, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

National audience; In this paper we evaluate the performance of 5 source separation algorithms (AMUSE, SOBI, SOBI-RO, SONS, JADE-TD) on simulated EEG signals. A first result evaluates the influence of the noise and signal characteristics (frequency, length, SNR) on the algorithms performance. A second objective is to introduce a new performance criterion, Eigen Values Vector's Norm-1 Distance (IEV) which can be used to compare two matrices and is potentially useful on real signals. We validate this new index by comparing it with classic performance indices used in source separation.

Published
2008

46. Processing and analysis of physiological sound signals. Application to phonoenterography

Author

Ranta, Radu, Ranta, Radu, Centre de Recherche en Automatique de Nancy (CRAN), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Institut National Polytechnique de Lorraine, Didier Wolf, Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Institut National Polytechnique de Lorraine - INPL, and Wolf Didier

Subjects
Analyse en composantes, [SPI.OTHER]Engineering Sciences [physics]/Other, wavelet detection and denoisng, principal component analysis, Traitement du signal, Non-stationary signals, localization, [SPI.AUTO]Engineering Sciences [physics]/Automatic, débruitage par ondelettes, Principales, phonoentérographie, extraction de caractéristiques, Signaux non-stationnaires, analyse en composantes principales, Ondelettes, Diagnostic, Wavelet detection and denosing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, diagnosis aid, phonoenterography, feature extraction, Non-stationary signais, [SPI.AUTO] Engineering Sciences [physics]/Automatic, localisation, classification, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, détection par ondelettes, aide au diagnostic, clustering
Abstract

The goal of this research is the development of an abdominal sound analysis system. The long-term objective is a diagnostic aid system based upon phonoenterogram analysis. The first step is the study of a specific multi-channel acquisition system. The following stage is the preprocessing: we implement an original wavelet fixed-point algorithm for the abdominal sounds detection, segmentation and denoising. A second preprocessing step introduces available a priori knowledge on physical characteristics of isolated abdominal sounds (main frequency, duration, acoustic intensity) and studies the problem of sound source localization. The extracted physical features, as well as a set of activity indexes appropriated for the global description of the phonoenterograms (number of sounds, mean energy, etc.), are used in the last processing stage, the signal analysis. The employed techniques are principal component analysis and unsupervised clustering., L'objectif de ce travail de recherche est le développement d'un système d'étude de sons, plus particulièrement dédié à la phonoentérographie, qui devrait aboutir à plus long terme à un outil d'aide au diagnostic. La première étape présente une chaîne d'instrumentation multi-voies spécifique. Elle est suivie par le pré-traitement: la détection, la segmentation et le débruitage par ondelettes sont réalisés avec un algorithme original optimisé par une méthode de point-fixe. Une deuxième phase introduit des connaissances a priori sur les sons abdominaux et étudie leur localisation spatiale. Les caractéristiques physiques (fréquence, intensité, durée) décrivent les sons individuellement. L'étude globale des phonoentérogrammes est réalisée à partir d'indices d'activité (nombre d'événements, énergie moyenne, etc.). Les caractéristiques physiques et les indices sont utilisés dans l'analyse statistique des signaux, par analyse en composantes principales et classification non supervisée.

Published
2003

47. Finally, what is the best filter for P300 detection?

Author

Bougrain, Laurent, Saavedra, Carolina, Ranta, Radu, Neuromimetic intelligence (CORTEX), 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), Centre de Recherche en Automatique de Nancy (CRAN), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), 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)-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
single-trial detection, low-pass filter, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.4: Applications/I.5.4.1: Signal processing, EEG, high-pass filter, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], P300, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; According to recent literature, the most appropriate preprocessing to improve P300 detection is still unknown or at least there is no consensus about it. Research papers refer to different low-pass filters, high-pass filters, baseline, subsampling or feature selection. In this paper, using a database with 23 healthy subjects we compare the effect on the letter accuracy (single-trial detection) provided by a linear support vector machine of a high-pass filter with cutoff frequencies from 0.1 to 1 Hz and a low-pass filter with cutoff frequencies from 8 to 60 Hz. According to this study, the best combination is for a band-pass filter of 0.1 to 15 Hz.

Published
2012

48. Hysteresis thresholding for Wavelet denosing applied to P300 single-trial detection

Author

Saavedra, Carolina, Bougrain, Laurent, Ranta, Radu, Neuromimetic intelligence (CORTEX), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Automatique de Nancy (CRAN), Université Henri Poincaré - Nancy 1 (UHP)-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), and INRIA

Subjects
Quantitative Biology::Neurons and Cognition, [SCCO.NEUR]Cognitive science/Neuroscience
Abstract

Template-based analysis techniques are good candidates to robustly detect transient temporal graphic elements (e.g. event-related potential, k-complex, sleep spindles, vertex waves, spikes) in noisy and multi-sources electro-encephalographic signals. More specifically, we present the significant impact on a large dataset of wavelet denoisings to detect evoked potentials in a single-trial P300 speller. We apply the classical thresholds selection rules algorithms and compare them with the hysteresis algorithm presented in \cite{Ranta10hyst} which combine the classical thresholds to detect blocks of significant wavelets coefficients based on the graph structure of the wavelet decomposition.

Published
2011

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