[SAN] Post-Doc Position
- From: Marco Congedo <loretabiofeedback@xxxxxxxxx>
- To: san-list@xxxxxxxxxxxxx
- Date: Wed, 22 Nov 2006 02:36:53 -0800 (PST)
Dear Colleagues,
please help us diffusing this post-doc offer as much as possible.
Thank you
Marco Congedo, PhD
http://www.novatecheeg.com/Congedo/MC_Home.html
Post-Doc Position
Where: Paris, PIC, Paris
Interdisciplanary Center
Centre de Reflexions
Interdisciplinaires (CRI)
Faculte de Medecine Universite Rene
Descartes-Paris V, Site Cochin Port-Royal
24, rue du Faubourg St Jacques 75014
PARIS
tel
: 01.44.41.22.69
When: As
soon as possible
Salary:
1800€/month net minimum (more depending on experience)
Contact: bernard.hennion@xxxxxxxxxxxxxxxxxx
Toward less-blind
source separation for brain computer interfaces
Supervisors:
Bernard HENNION,
PhD, France
Telecom Research and Development (FTR&D)
Ivo SBALZARINI,
PhD, Assistant Professor, ETH Zurich,
Computational Biophysics Lab
1. Background
Today, different blind source
separation techniques are used to analyze non-invasive EEG or MEG signals in
brain computing interfaces. All of them are based on the hypothesis of
exact knowledge of the direct transmission matrix through the gray
matter. This matrix describes the propagation of the electromagnetic waves from
the active brain regions to the locations of the measurement points. The
signals describe the activation of localized electromagnetic dipoles, resulting
from the cumulative, synchronous, and simultaneous action of several tens of
thousands of parallel oriented neurons, as, e.g., seen in cortical columns.
In practice, the direct
transmission matrix is, however, unknown, such that the hypothesis
underlying blind source separation is hardly true. In addition, a good
part of what is usually called "measurement noise" actually contains
a significant proportion of signal energy, owing nothing to hazard, but rather
containing the neural signals of multiple clusters of small numbers of neurons,
or neurons of random directional orientation, such that their signal
contributions
cannot be accurately detected by the measurement on the skull surface.
2. Research proposal
In the present study, we
propose to use a powerful direct numerical simulation technique, running
on a large number of parallel processors, to simulate the accurate
intra-skull propagation of electromagnetic waves. Our simulations will
be directly based on first principles from physics (electromagnetics, wave
propagation, material properties) and we will use actual and accurate 3D
geometries from real brains and skulls as determined by tomographic imaging.
This will allow, for the first time, to explicitly account for effects such as
smearing on internal faces of skull bones, anisotropy of the brain matter,
different physical diffusion parameter values of white matter, grey matter,
cerebellum trunk, meninges, cephalo-rachidian liquid, bones, blood circulation,
muscle activity signals, skin effects, effects of the convoluted geometric
shape of the brain cortex, cross-talk between measurement points, and the exact
positioning of the measurement points. In addition, the interface effects at
each material frontier that the electromagnetic waves cross can be investigated
and accounted for.
The proposed physical direct
(forward) simulations allow determining an accurate direct transmission
matrix, describing the signal propagation from brain voxels to scalp
pixels. The simulations are directly based on the proper physical laws of
electromagnetic wave propagation through the "details" of intra-skull
anatomical geometries. Due to the large number of brain voxels (250 000 with an
edge-length of 8mm) and skull pixels (230 000 with an edge-length of 1mm), the
complex geometry of the brain, and the different involved materials, the
simulations will require the use of parallel supercomputing resources
with hundreds of processors. We will thus implement the simulation program on
the basis of an existing parallel programming framework, called the PPM Library
[1]. This library allows automatic parallelization of scientific simulations
and effectively reduces the code development time by several orders of
magnitude (as an example, a parallel discrete element simulation for granular
flow was implemented within 2 person-weeks, whereas the normal development time
for such a code would have been 2 person-years). The PPM library has
demonstrated parallel efficiencies and computational speeds that meet or
exceed the present state of the art in many fields, including
computational fluid dynamics, molecular dynamics, computational cell biology,
and astrophysics [1]. The present project will greatly benefit from the use of
this library.
In order to ensure accurate
and efficient simulations, we propose to use adaptive multi-resolution
particle methods to discretize the governing equations [2,5]. Particle
methods have unique characteristics [5] that allow simulations in complex
geometries [3], simulations on multiple spatial scales (i.e. to achieve finer
resolution in the cortex than in the white matter) [2,5], and have favorable
robustness properties with respect to noise and instabilities.
The direct transmission
matrix for a specific brain geometry (i.e., person) can then be
identified using the proposed simulations. Therefore, we will independently
simulate the measured read-out on the skull (at the positions of the
measurement points) for point-source signals originating from each of the brain
voxels. This yields the individual point transmission matrices that can then be
superimposed in order to obtain the full direct transmission matrix (Green’s
function superposition principle).
This accurate transmission
matrix can be inverted numerically in order to obtain its inverse.
Multiplication of the EEG or MEG measurements with this inverse then directly
yields the activity distribution in the whole volume of the brain. The
spatial resolution will initially be 8mm, but can later be improved. The
achievable resolution is only limited by the available computer resources.
Since the PPM library has been successfully used to perform simulations on up
to 1024 processors using up to 1 billion computational elements [1],
unprecedented resolutions will be possible.
We will validate our
simulations using experimental data. These will involve both invasive
(brain) and non-invasive (skull) EEG measurement as, e.g., collected from
epileptic patients prior to surgery. Application of the determined transmission
matrix to the invasive measurements should recover the non-invasive signals.
Comparison of this outcome with the actually measured non-invasive signals then
enables us to compute differences between the simulation model and reality.
Using a black-box optimization strategy such as, e.g., Evolution Algorithms
[4], we will then adjust the material constants and simulation parameters in
the computer (but not the actual physical model), such that the simulations
match the experiment. The so-determined constants and parameters constitute a
valuable outcome of the proposed project by themselves, as they are largely
unknown for brain/skull material today. In addition, experiments and
simulations will use the exact same brain geometry (individualized for a
specific person). This will allow, for the first time, the study of
inter-personal variations in the physical parameters and the brain signal
propagation dynamics.
3. Project
situation and significance
The present project is highly
interdisciplinary and situated within the Openvibe project of real-time
3D analysis and visualisation of brain activity from scalp signals. The
long-term goal of the Openvibe project is to ameliorate mental command comfort.
More accurate transmission matrices, as provided by the present project, are of
crucial importance in achieving this goal.
The following scientific fields and expertises are involved in the
present project:
-
High-performance
parallel computing/grid computing and software engineering (see point 4 of
FTR&D/INSERM agreement)
-
Development
of adaptive multi-resolution numerical methods for the simulation of complex
dynamic systems
-
Precise
modelling of living matter, better understanding of brain activity and the
process of thought (see point 10 of FTR&D/INSERM agreement)
-
Brain
computing interface techniques (Inverse mathematic problem, sLoreta, stochastic
analysis and de-correlation of independent sources)
-
Mental
control comfort (As an example, infrared remotes were, at their origin, designed
for handicapped persons. Nowadays, every piece of home-electronic equipment
uses them…)
-
Accessibility
ergonomics
-
France
Myopath Association, AFM as a research partner.
The present project
requires a union of skills and scientific expertise in the following fields:
- Ergotherapeut (AFM Claude Dumas, Ornella PLOS PhD)
-
Cognitive
ergonomy (Accessibility, Denis Chêne, FTR&D)
-
Neuroscience
and neural biology (Olivier Bertrand, Jean-Philippe Lachaux, Inserm Lyon, Marco
congedo, INPG)
-
Mathematics:
probability and stochastic signals analysis (Christian Jutten, Cedric PhD, INPG)
-
Physics,
electromagnetism, and interface effects (Nelido Gonzalez-Segredo, proposed
postdoctoral fellow)
-
Brain
physiology, psychiatry, anatomy, EEG/MEG measurement (Ms. Garnero, Pitié
Salpêtrière, physiciens du CRI)
-
Parallel
numerical simulations, (Bernard Hennion FTR&D/CRI, Ivo F. Sbalzarini
ETH/CRI, Birte Schrader ETH)
-
Parallel
software engineering and programming (Ivo Sbalzarini ETH/CRI, Birte Schrader
ETH, PPM framework)
- Implementation of adaptive multi-resolution particle
methods (Ivo F. Sbalzarini ETH/CRI, Birte Schrader ETH)
Besides delivering more accurate and individualized brain activity
transmission matrices, the proposed project will also contribute to answering
the following open questions:
-
What
is the best hardware/software architecture to perform such simulations?
-
How
can existing architectures be improved and further developed to meet the future
needs in high-performance computing and networking?
-
What
accurate direct transmission matrix is to be injected in real-time inverse
problems
for brain computing interfaces?
-
What
will be the eventual customisation of these direct matrices according to
different
head/brain shapes?
-
How
large are the inter-personal fluctuations in geometry, physical parameters, and
transmission matrix?
-
What
is the best way of validating models of human brain activity?
-
What
are the most comfortable mental control commands?
4. Publication
potential
Due to its interdisciplinary
nature (medicine, psychology, psychiatry, computational science, numerical
mathematics, physics, engineering), we believe that the present project offers
interesting publication opportunities. Given the broad interest in
the topic, from hospitals over brain research to the telecommunication
industry, we expect that more than one publication will appear in an
interdisciplinary
scientific journal of high profile and visibility. 6 to 8 other articles
could be published in international journals of biophysics (material parameters
and wave propagation dynamics in brain tissue, simulating and calibrating EEG
and MEG measurements), computational science (adaptive multi-resolution
particle method, parallel high-performance brain simulations), medicine (brain
imaging, inter-person variations of brain parameters and geometries),
psychiatry (brain imaging), and engineering (brain computer interface).
Since the results and data
produced are expected to find numerous applications beyond the scope of the
present project, the publications bear the potential to be cited
frequently.
5. Requested
resources
To complement the project
team, we are looking for a full-time researcher on the postdoctoral level.
The requested person will form the core of the proposed project, integrating
and coordinating all the participating groups and researchers. As such, we are
looking for a person that unites expertise in physics, applied mathematics,
parallel computing, and biological/medical modeling.
The proposed interdisciplinary
post-doc will be able to discuss with specialists from all the different
fields, gather geometric data of different head tropisms, formulate the
physical models, and help implementing the simulation code using the PPM
framework. He will be the principal person in modelling, performing the actual
simulations, validating them, and discussing the results.
6. References
[1] I. F.
Sbalzarini, J. H. Walther, M. Bergdorf, S. E. Hieber, E. M.
Kotsalis, and P. Koumoutsakos. PPM – a highly efficient parallel
particle-mesh library for the simulation of continuum systems. J. Comput.
Phys., 215(2):566–588, 2006.
[2]
M. Bergdorf, G.-H. Cottet, and P. Koumoutsakos. Multilevel adaptive
particle methods for convection-diffusion equations. Multiscale Model.
Simul., 4(1):328–357, 2005.
[3]
I. F. Sbalzarini, A. Mezzacasa,
A. Helenius, and P. Koumoutsakos. Effects of organelle shape on
fluorescence recovery after photobleaching. Biophys. J.,
89(3):1482–1492, 2005.
[4]
N. Hansen and A. Ostermeier. Adapting arbitrary normal mutation
distributions in evolution strategies: The covariance matrix adaptation. In
Proceedings
of the 1996 IEEE Conference on Evolutionary Computation (ICEC ’96), pages
312–317, 1996.
[5]
P. Koumoutsakos. Multiscale flow simulations using particles. Annu.
Rev. Fluid Mech., 37:457–487, 2005.
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