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

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 [email protected]

OpenMEEG: opensource software for quasistatic bioelectromagnetics

Background: Interpreting and controlling bioelectromagnetic phenomena require realistic physiological models and accurate numerical solvers. A semi-realistic model often used in practise is the piecewise constant conductivity model, for which only the interfaces have to be meshed. This simplified model makes it possible to use Boundary Element Methods. Unfortunately, most Boundary Element solutions are confronted with accuracy issues when the conductivity ratio between neighboring tissues is high, as for instance the scalp/skull conductivity ratio in electro-encephalography. To overcome this difficulty, we proposed a new method called the symmetric BEM, which is implemented in the OpenMEEG software. The aim of this paper is to presen

Cortical mapping by Laplace-Cauchy transmission using a boundary element method.

International audienceThe Laplace-Cauchy problem of propagating Dirichlet and Neumann data from a portion to the rest of the boundary is an ill-posed inverse problem. Many regularizing algorithms have been recently proposed, in order to stabilize the solution with respect to noisy or incomplete data. Our main application is in electro-encephalography (EEG) where potential measurements available at part of the scalp are used to reconstruct the potential and the current on the inner skull surface. This problem, known as cortical mapping, and other applications --- in fields such as nondestructive testing, or biomedical engineering --- require to solve the problem in realistic, three-dimensional geometry. The goal of this article is to present a new boundary element based method for solving the Laplace-Cauchy problem in three dimensions, in a multilayer geometry. We validate the method experimentally on simulated data

Adaptive classification of mental states for asynchronous brain computer interfaces

ISBN : 978-2-9532965-0-1Brain Computers Interfaces (BCI) are emerging as a new communicational device, aiming to make a direct link between the brain and an external device, bypassing conventional motor outputs, such as peripheral nerves and muscles. A BCI extracts features from a brain signal and classifies them in order to interpret them in terms of the user's volition. For communication to be effective, the computer has to provide feedback to the user allowing him/her to judge how the brain activity is being classified and interpreted. Similarly, the user must produce patterns of brain activity which can easily be learned and recognized by the computer. Here, we describe a method for selecting mental tasks that are best classified by a subject using support vector machines (SVM)

A Feasibility Study: Operator Splitting for Solving Anisotropic Problem

The electroencephalography (EEG) is a non-invasive technique to study electrical brain activity (while the brain is performing a cognitive task). The electrical brain activity is a complex process of electrical propagation because the brain structure is an incredibly complex structure. This complex structure leads to different conductivity properties in terms of its magnitude and orientation, called anisotropic conductivity. Using Maxwell's equations, electrical brain activity has been studied intensively. For simplification, the quasistatic Maxwell’s equations are used to model the electrical brain activity and it leads to deal with a Poisson’s equation. In this research, a feasibility study of using a new method, called Operator Splitting Method (OSM), to solve anisotropic 2-Dimensional (2D) Poisson’s equation is performed. Freeware of the finite element method (FEM), FreeFEM++, is employed to build matrices used in the OSM algorithm. The OSM algorithm which is written in Matlab is then tested to solve anisotropic 2D Laplace’s equation and anisotropic Poisson’s equation with the dipolar source. Afterward, the OSM solutions are validated by using an exact solution and a direct numerical solution. By using L2-Error Norm, the convergence rate of the OSM algorithm is then analyzed. Some numerical experiments have been performed to test the performance of the OSM algorithm. The OSM solution of anisotropic 2D Laplace’s equation coincides with the exact and direct numerical solution of the problem. For anisotropic 2D Poisson’s equation with dipolar source, some similar results have been obtained too. The pattern of the OSM solutions is similar to the pattern of direct numerical solutions of the problem. The results arise a hope to attempt to implement the OSM algorithm for more complex problems such as a realistic human head model

Adaptive and Warning Displays with Brain-Computer Interfaces : Enhanced Visuospatial Attention Performance

Some parts of this work have been covered by a patent, application (n° 13 60563) at Institut National de la Propriété Intellectuelle (INPI)International audienceBrain-Computer Interfaces (BCI) can provide innovative solutions beyond the medical domain. In human research, visuospatial attention is often assessed from shifts in head or gaze orientation. However in some critical situations, these behavioral features can be dissociated from covert attention processes and brain features may indicate more reliably the spatial focus of attention. In this context, we investigate whether EEG signals could be used to enhance the behavioral performance of human subjects in a visuospatial attention task. Our results demonstrate that a BCI protocol based on adaptive or warning displays can be developed to shorten the reaction time and improve the accuracy of responses to complex visual targets

Enhancing Visuospatial Attention Performance with Brain-Computer Interfaces

International audienceVisuospatial attention is often investigated with features related to the head or the gaze during Human-Computer Interaction (HCI). However the focus of attention can be dissociated from overt responses such as eye movements, and impossible to detect from behavioral data. Actually, Electroencephalography (EEG) can also provide valuable information about covert aspects of spatial attention. Therefore we propose a innovative approach in view of developping a Brain-Computer Interface (BCI) to enhance human reaction speed and accuracy. This poster presents an offline evaluation of the approach based on physiological data recorded in a visuospatial attention experiment. Finally we discuss about the future interface that could enhance HCI by displaying visual information at the focus of attention

Augmenting Motor Imagery Learning for Brain–Computer Interfacing Using Electrical Stimulation as Feedback

International audienceBrain-computer Interfaces (BCI) and Functional electrical stimulation (FES) contribute significantly to induce cortical learning and to elicit peripheral neuronal activation processes and thus, are highly effective to promote motor recovery. This study aims at understanding the effect of FES as a neural feedback and its influence on the learning process for motor imagery tasks while comparing its performance with a classical visual feedback protocol. The participants were randomly separated into two groups: one group was provided with visual feedback (VIS) while the other received electrical stimulation (FES) as feedback. Both groups performed various motor imagery tasks while feedback was provided in form of a bi-directional bar for VIS group and targeted electrical stimulation on the upper and lower limbs for FES group. The results shown in this paper suggest that the FES based feedback is more intuitive to the participants, hence, the superior results as compared to the visual feedback. The results suggest that the convergence of BCI with FES modality could improve the learning of the patients both in terms of accuracy and speed and provide a practical solution to the BCI learning process in rehabilitation

Topography-Time-Frequency Atomic Decomposition for Event-Related M/EEG Signals.

International audienceWe present a method for decomposing MEG or EEG data (channel x time x trials) into a set of atoms with fixed spatial and time-frequency signatures. The spatial part (i.e., topography) is obtained by independent component analysis (ICA). We propose a frequency prewhitening procedure as a pre-processing step before ICA, which gives access to high frequency activity. The time-frequency part is obtained with a novel iterative procedure, which is an extension of the matching pursuit procedure. The method is evaluated on a simulated dataset presenting both low-frequency evoked potentials and high-frequency oscillatory activity. We show that the method is able to recover well both low-frequency and high-frequency simulated activities. There was however cross-talk across some recovered components due to the correlation introduced in the simulation