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302 results on '"Clerc, Maureen"'

3. Optimized application of double and single layer BEM for in vivo conductivity estimation

Author

de Munck, Jan C, Daffertshofer, Andreas, Montes-Restrepo, Victoria, Faes, Theo JC, Clerc, Maureen, and Hulshof, Joost

Subjects
Physics - Medical Physics
Abstract

Inter subject variability of the electrical conductivity of brain, skull and skin strongly limits the accuracy by which current sources underlying electro-encephalography (EEG) can be localized in the brain. This inter subject variability also constrains the possibility to use EEG amplitude parameters as a biomarker to compare the amount of neural activity between different patients. To overcome this problem, one may estimate conductivity parameters in vivo by analyzing the potentials generated by known electric currents, injected into different pairs of EEG electrodes. At present, routine application of this approach is hampered by the computational complexity of the conductivity estimation problem. Here we analyze the efficiency of this conductivity parameter estimation problem in the context of boundary element method (BEM). We assume geometries of brain, skull and skin compartments are fixed triangular meshes whereas conductivity parameters are treated as unknowns. We show that a Woodbury update algorithm can be used to obtain a fast conductivity update scheme for both the single and double layer BEM formalism. This algorithm yields a speed gain up to a factor of 20 when compared to the direct computations, apart from at most 50% of additional computation time in the initialization phase of the algorithm. We also derive novel analytically closed expressions for the efficient and accurate computation of BEM matrix elements. Finally, we discuss which further steps are needed to equip future EEG systems with software devices that enable subject tailored head models for calibrated EEG and accurate source localization, on a routine basis., Comment: 25 pages, 5 figures

Published
2019

4. Data-driven cortical clustering to provide a family of plausible solutions to M/EEG inverse problem

Author

Maksymenko, Kostiantyn, Clerc, Maureen, and Papadopoulo, Théodore

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning
Abstract

The M/EEG inverse problem is ill-posed. Thus additional hypotheses are needed to constrain the solution space. In this work, we consider that brain activity which generates an M/EEG signal is a connected cortical region. We study the case when only one region is active at once. We show that even in this simple case several configurations can explain the data. As opposed to methods based on convex optimization which are forced to select one possible solution, we propose an approach which is able to find several "good" candidates - regions which are different in term of their sizes and/or positions but fit the data with similar accuracy.

Published
2018

5. Fast Approximation of EEG Forward Problem and Application to Tissue Conductivity Estimation

Author

Maksymenko, Kostiantyn, Clerc, Maureen, and Papadopoulo, Théodore

Subjects
Computer Science - Computational Engineering, Finance, and Science, Mathematics - Numerical Analysis
Abstract

Bioelectric source analysis in the human brain from scalp electroencephalography (EEG) signals is sensitive to the conductivity of the different head tissues. Conductivity values are subject dependent, so non-invasive methods for conductivity estimation are necessary to fine tune the EEG models. To do so, the EEG forward problem solution (so-called lead field matrix) must be computed for a large number of conductivity configurations. Computing one lead field requires a matrix inversion which is computationally intensive for realistic head models. Thus, the required time for computing a large number of lead fields can become impractical. In this work, we propose to approximate the lead field matrix for a set of conductivity configurations, using the exact solution only for a small set of basis points in the conductivity space. Our approach accelerates the computing time, while controlling the approximation error. Our method is tested for brain and skull conductivity estimation , with simulated and measured EEG data, corresponding to evoked somato-sensory potentials. This test demonstrates that the used approximation does not introduce any bias and runs significantly faster than if exact lead field were to be computed., Comment: Copyright (c) 2019 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org

Published
2018

6. Zero-calibration cVEP BCI using word prediction: a proof of concept

Author

Turi, Federica, Gayraud, Nathalie, and Clerc, Maureen

Subjects
Computer Science - Human-Computer Interaction, Quantitative Biology - Neurons and Cognition
Abstract

Brain Computer Interfaces (BCIs) based on visual evoked potentials (VEP) allow for spelling from a keyboard of flashing characters. Among VEP BCIs, code-modulated visual evoked potentials (c-VEPs) are designed for high-speed communication . In c-VEPs, all characters flash simultaneously. In particular, each character flashes according to a predefined 63-bit binary sequence (m-sequence), circular-shifted by a different time lag. For a given character, the m-sequence evokes a VEP in the electroencephalogram (EEG) of the subject, which can be used as a template. This template is obtained during a calibration phase at the beginning of each session. Then, the system outputs the desired character after a predefined number of repetitions by estimating its time lag with respect to the template. Our work avoids the calibration phase, by extracting from the VEP relative lags between successive characters, and predicting the full word using a dictionary., Comment: BCI 2018 - 7th International BCI Meeting, May 2018, Pacific Grove, California, United States

Published
2018

8. Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals

Author

Hitziger, Sebastian, Clerc, Maureen, Gramfort, Alexandre, Saillet, Sandrine, Bénar, Christian, and Papadopoulo, Théodore

Subjects
Statistics - Machine Learning
Abstract

Dictionary Learning has proven to be a powerful tool for many image processing tasks, where atoms are typically defined on small image patches. As a drawback, the dictionary only encodes basic structures. In addition, this approach treats patches of different locations in one single set, which means a loss of information when features are well-aligned across signals. This is the case, for instance, in multi-trial magneto- or electroencephalography (M/EEG). Learning the dictionary on the entire signals could make use of the alignement and reveal higher-level features. In this case, however, small missalignements or phase variations of features would not be compensated for. In this paper, we propose an extension to the common dictionary learning framework to overcome these limitations by allowing atoms to adapt their position across signals. The method is validated on simulated and real neuroelectric data., Comment: 9 pages, 5 figures, minor corrections

Published
2013

9. Solving the Cross-Subject Parcel Matching Problem Using Optimal Transport

Author

Gallardo, Guillermo, Gayraud, Nathalie T. H., Deriche, Rachid, Clerc, Maureen, Deslauriers-Gauthier, Samuel, Wassermann, Demian, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Frangi, Alejandro F., editor, Schnabel, Julia A., editor, Davatzikos, Christos, editor, Alberola-López, Carlos, editor, and Fichtinger, Gabor, editor

Published
2018
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18. Adaptive Time-Frequency Models for Single-Trial M/EEG Analysis

Author

Bénar, Christian, Clerc, Maureen, Papadopoulo, Théodore, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Karssemeijer, Nico, editor, and Lelieveldt, Boudewijn, editor

Published
2007
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23. Wavelet-Based Correlation for Stereopsis

Author

Clerc, Maureen, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Heyden, Anders, editor, Sparr, Gunnar, editor, Nielsen, Mads, editor, and Johansen, Peter, editor

Published
2002
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24. Investigating the origin of phase delays in the alpha band

Author

Le Breton, Côme, Papadopoulo, Théodore, Clerc, Maureen, Service Expérimentation et Développement (SED [Sophia]), Institut National de Recherche en Informatique et en Automatique (Inria), ATHENA (ATHENA), Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Inria Sophia Antipolis - Méditerranée (CRISAM), and Le Breton, Côme

Subjects
[SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience
Abstract

International audience; Traveling (or propagating) wave are oscillating electrical voltages observed sequentially or simultaneously at different brain locations, relating them functionally. They are observed at various scales, all over the brain, in various frequency bands and following varied stimuli (or in their absence), making them an ubiquitous phenomenon. Ermentrout and Kleinfeld [2001] proposed three possible mechanisms: 1) A unique oscillator activating cortical areas with different timedelays (resulting in a spurious wave), 2) A chain of successively activated oscillators, 3) Coupled local oscillators generating wave propagation due to phase delay. In scalp EEG, volume conduction considerably complicates the problem, and allows to consider a fourth “mechanism”, in which only two sources can generate phase delays at many electrodes. We show that phase differences in the α band appear along the anteroposterior axis during the ECR condition, and this pattern is stable across recordings taken years apart. A simplistic model of two sources is enough to model phase differences incompatible with transmission delays. Care must therefore be brought to the apriori misconception that PLI index is not sensitive to volume conduction.

Published
2022

32. Phase Synchrony Neurofeedback ? BEWARE !

Author

Le Breton, Côme, Papadopoulo, Théodore, Clerc, Maureen, Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Ludwig-Maximilians-Universität München, European Project: 694665,H2020 ERC,ERC-2015-AdG,CoBCoM(2016), Le Breton, Côme, and Computational Brain Connectivity Mapping - CoBCoM - - H2020 ERC2016-09-01 - 2021-08-31 - 694665 - VALID

Subjects
[SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience
Abstract

International audience; Assessing properly phase synchrony is a real challenge, even for offline analysis. It has mostly to do with the volume conduction / volume leakagemixing , the uncertainty principle and the redundancy of the wavelet coefficients. One or more of these issues are often neglected. We provide a framework for better understanding and interpreting phase extraction with the Morlet Wavelet Transform to show that errors are made when extracting phase information and must be accounted for. Before attempting to blindly use phase synchrony measures in Neurofeedback, there a fortiori a need for a proper and efficient estimation of the statistical significance of the measures calculated on the fly.

Published
2021

36. A global sensitivity analysis of three- and four-layer EEG conductivity models

Author

Vallaghe, Sylvain and Clerc, Maureen

Subjects
Electroencephalography -- Methods, Accuracy and precision -- Research, Impedance, Bioelectric -- Observations, Electrical conductivity -- Models, Electrophysiology -- Research, Tomography -- Methods, Biological sciences, Business, Computers, Health care industry
Abstract

The accuracy of forward models for EEG partly depends on the conductivity values of the head tissues. Yet, the influence of the conductivities on the model output is still not well understood. In this paper, we apply a variance-based sensitivity analysis method to the most common EEG forward models (three or four layers). This method is global because it quantifies the influence of each parameter with all the parameters varying at the same time. With nonlinear models, it helps to understand the interaction between parameters, which is not possible with simple sensitivity analyses (one-at-a-time variations, derivatives, and perturbations). By analyzing the potential topographies at the electrodes, we obtained several results. For a shallow dipole, the EEG topographies are mainly sensitive to the interaction between skull and scalp conductivities. It means that the variability of the EEG topographies is driven mostly by a function of skull and scalp conductivities. Similar results are presented for skull anisotropy and a current injection as performed in electrical impedance tomography. This global sensitivity analysis gives new information about EEG forward models--it identifies the main input parameters that need model refinement--and directions on how to calibrate these models. Index Terms--Conductivity, EEG, electrical impedance tomography (EIT), sensitivity analysis, variance-based methods.

Published
2009

37. Auto-calibration of c-VEP BCI by word prediction

Author

Turi, Federica, Gayraud, Nathalie, Clerc, Maureen, Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Côte d'Azur (UCA), and Turi, Federica

Subjects
[SDV.IB] Life Sciences [q-bio]/Bioengineering, [INFO.INFO-SY] Computer Science [cs]/Systems and Control [cs.SY], [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, Brain Computer Interface, [SCCO] Cognitive science, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [SCCO]Cognitive science, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], auto-calibration, [INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY], [SDV.IB]Life Sciences [q-bio]/Bioengineering, c-VEP BCI, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, word prediction
Abstract

A code-modulated Visual Evoked Potential Brain Computer Interface (c-VEP BCI) allows for spelling from a virtual keyboard of flashing characters. All characters flash simultaneously, and each character flashes according to a predefined pseudo-random binary sequence, circular-shifted by a different time lag. For a given character, the pseudo-random stimulus sequence evokes a VEP in the electroencephalogram (EEG) of the subject, which can be used as a template. This template is usually obtained during a calibration phase and it is applied for the target identification during the spelling phase. A downside of a c-VEP BCI system is that it needs a long calibration phase to reach good performance. This paper proposes an unsupervised method that avoids the calibration phase in a c-VEP BCI, by extracting relative lags from the VEP responses, between successive characters, and predicting the full word using a dictionary. We tested it in offline experiments on a public dataset. We simulated the spelling of four groups of words with a different total number of characters selected from an English dictionary. Each experiment is parameterized by the number of stimulus cycles. The obtained results show that a word-prediction-based auto-calibration method in c-VEP BCIs can be efficient and effective.

Published
2020

40. A common formalism for the integral formulations of the forward EEG problem

Author

Kybic, Jan, Clerc, Maureen, Abboud, Toufic, Faugeras, Olivier, Keriven, Renaud, and Papadopoulo, Theo

Subjects
Poisson's equation -- Usage, Electroencephalography -- Methods, Business, Electronics, Electronics and electrical industries, Health care industry
Abstract

The forward electroencephalography (EEG) problem involves finding a potential V from the Poisson equation [nabla] x ([sigma][nabla]V) = f, in which f represents electrical sources in the brain, and [sigma] the conductivity of the head tissues. In the piecewise constant conductivity head model, this can be accomplished by the boundary element method (BEM) using a suitable integral formulation. Most previous work uses the same integral formulation, corresponding to a double-layer potential. In this paper we present a conceptual framework based on a well-known theorem (Theorem 1) that characterizes harmonic functions defined on the complement of a bounded smooth surface. This theorem says that such harmonic functions are completely defined by their values and those of their normal derivatives on this surface. It allows us to cast the previous BEM approaches in a unified setting and to develop two new approaches corresponding to different ways of exploiting the same theorem. Specifically, we first present a dual approach which involves a single-layer potential. Then, we propose a symmetric formulation, which combines single- and double-layer potentials, and which is new to the field of EEG, although it has been applied to other problems in electromagnetism. The three methods have been evaluated numerically using a spherical geometry with known analytical solution, and the symmetric formulation achieves a significantly higher accuracy than the alternative methods. Additionally, we present results with realistically shaped meshes. Beside providing a better understanding of the foundations of BEM methods, our approach appears to lead also to more efficient algorithms. Index Terms--Boundary element method, EEG, integral method, Poisson equation.

Published
2005

43. The texture gradient equation for recovering shape from texture

Author

Clerc, Maureen and Mallat, Stephane

Subjects
Stochastic processes -- Models, Pattern recognition -- Models
Abstract

This paper studies the recovery of shape from texture under perspective projection. We regard Shape from Texture as a statistical estimation problem, the texture being the realization of a stochastic process. We introduce warplets, which generalize wavelets over the 2D affine group. At fine scales, the warpogram of the image obeys a transport equation, called Texture Gradient Equation. In order to recover the 3D shape of the surface, one must estimate the deformation gradient, which measures metric changes in the image. This is made possible by imposing a notion of homogeneity for the original texture, according to which the deformation gradient is equal to the velocity of the Texture Gradient Equation. By measuring the warplet transform of the image at different scales, we obtain a deformation gradient estimator. Index Terms--Shape from texture, texture gradient, warplets, wavelets.

Published
2002

46. OpenMEEG software for forward problems, coping with non-nested geometries

Author

Clerc, Maureen, Gramfort, Alexandre, Papadopoulo, Théodore, Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), 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), European Project: 694665,H2020 ERC,ERC-2015-AdG,CoBCoM(2016), Clerc, Maureen, Computational Brain Connectivity Mapping - CoBCoM - - H2020 ERC2016-09-01 - 2021-08-31 - 694665 - VALID, Service NEUROSPIN (NEUROSPIN), and 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

Subjects
[SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
Abstract

International audience; OpenMEEG implements boundary element solutions for simulating electromagnetic fields in the quasistaticregime. Originally designed for the forward EEG and MEG problems (MEEG collectively), it has also beenapplied to compute forward solutions for ECoG, for implanted EEG, for cochlear implant stimulation, for tDCSand other electrostimulation settings. In this presentation we detail the features of the latest release ofOpenMEEG.Geometrical models have now been extended to handle non-nested geometries: the various domains muststill have a constant isotropic conductivity but they need no longer be nested inside one another. OpenMEEGsupports CGAL meshing tools (allowing to remesh or decimate existing meshes, or to mesh a levelset). Linearalgebra packages MKL and OpenBLAS are now supported on all platforms (Linux, MacOS and Windows).Interface with python is improved with python wrappers that avoid memory loss. Gifti and VTK mesh formatsare supported, and some visualization tools are provided (VTK or mayavi). More tests have been included,and last but not least, some bugs fixed.

Published
2018

47. Conductivity models for functional neuroimaging

Author

Clerc, Maureen, Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), European Project: 694665,H2020 ERC,ERC-2015-AdG,CoBCoM(2016), Clerc, Maureen, and Computational Brain Connectivity Mapping - CoBCoM - - H2020 ERC2016-09-01 - 2021-08-31 - 694665 - VALID

Subjects
[SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging
Abstract

International audience; Conductivity models for functional neuroimaging Electro-and magneto-encephalography provide measurements of brain activity at high time resolution. Their interpretation is intricate because they mix various sources of activity. This talk establishes the models required for extracting brain source activity from EEG and MEG data. To represent the geometry and conductivity of tissues, and compute the forward problems, boundary element methods have been developped and implemented in opensource software. This is of particular interest for multimodal validation of source localization results.

Published
2018

48. Inria Research & Development for the Cybathlon BCI series

Author

Lotte, Fabien, Clerc, Maureen, Appriou, Aurélien, Audino, Amandine, Benaroch, Camille, Giacalone, Pierre, Jeunet, Camille, Mladenović, Jelena, Monseigne, Thibaut, Papadopoulo, Théodore, Pillette, Léa, Roc, Aline, Sadatnejad, Khadijeh, Turi, Federica, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), Cognition, Langues, Langage, Ergonomie (CLLE), Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Toulouse - Jean Jaurès (UT2J), ANR-15-CE23-0013,REBEL,Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle(2015), European Project: 714567 ,H2020 Pilier ERC,BrainConquest(2017), Lotte, Fabien, Interactions humain-machine, objets connectés, contenus numériques, données massives et connaissance - Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle - - REBEL2015 - ANR-15-CE23-0013 - AAPG2015 - VALID, Boosting Brain-Computer Communication with high Quality User Training - BrainConquest - - H2020 Pilier ERC2017-07-01 - 2022-06-30 - 714567 - VALID, Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, École Pratique des Hautes Études (EPHE), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), and Université de Toulouse (UT)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO]Cognitive science, [INFO.INFO-BT] Computer Science [cs]/Biotechnology, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [SCCO] Cognitive science, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-BT]Computer Science [cs]/Biotechnology, ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience

Published
2019

49. Adaptive parameter setting in a code modulated visual evoked potentials BCI

Author

Turi, Federica, Clerc, Maureen, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Turi, Federica

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [SCCO.COMP] Cognitive science/Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC]
Abstract

International audience; Code-modulated visual evoked potentials (c-VEPs) BCI are designed for high-speed communication. The setting of stimulus parameters is fundamental for this type of BCI, because stimulus parameters have an influence on the performance of the system. In this work we design a c-VEP BCI for word spelling, in which it is possible to find the optimal stimulus presentation rate per each subject thanks to an adaptive setting parameter phase. This phase takes place at the beginning of each session and allows to define the stimulus parameters that are used during the spelling phase. The different stimuli are modulated by a binary m-sequence circular-shifted by a different time lag and a template matching method is applied for the target detection. We acquired data from 4 subjects in two sessions. The results obtained for the offline spelling show the variability between subjects and therefore the importance of subject-dependent adaptation of c-VEP BCI.

Published
2019

50. Technology Brain Computer Interface for Autonomy: Patient-adapted ergonomic headset for dry-EEG P300 speller

Author

Guebba, Sofiane, Guy, Violaine, Papadopoulo, Théodore, Bruno, Marianne, Clerc, Maureen, Soriani, Marie-Hélène, Université Côte d'Azur (UCA), Centre de Référence Maladies Neuromusculaires et SLA, hôpital Archet 1, Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut National de Recherche en Informatique et en Automatique (Inria), and Papadopoulo, Théodore

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging
Abstract

International audience; Recent work demonstrates feasibility for ALS patients to communicate via P300 speller [1], but end-user's expectations are not met concerning comfort and ease of use, particularly for patient under heavy pathology [8]. One challenge for daily use [8],[9] is to provide patients with gel-free headsets that are easy to put on and comfortable while providing enough signal quality for accurate P300 classification. Our proposal is to custom design a silicone headset to fit patient's head morphology [5], paying attention on good pressure distribution and hair layer penetration. We will firstly show feasibility of Dry P300Speller with State-of-the-Art amplifier, then analyze electrode impedance variation over time while the headset is worn, and its relation to P300 classification accuracy.

Published
2019

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