Activity Report 2010 - CNRS

OVERVIEW
Tetiana AKSENOVA - A leading expert
involved in the unique world class
program CLINATEC ® .
Previously engineerresearcher
at INSERM,
U318 CHU Grenoble
(from 2002 to 2007),
Tetiana AKSENOVA, 53,
came back to Grenoble
thanks to a full time
Chair of Excellence
granted through the
Nanosciences Foundation’s 2008 Call.
On leave from Ukrainian Academy of
Sciences, and presently at Léti, Tetiana is
also the co-founder of PNN-Soft. Her
group leader proposed CEA to hire
Tetiana full time at the end of the
Nanosciences Foundation’s support.
The procedure of adaptive
calibration is aimed to the fasten BCI
system installation and recalibration. In
2010 the principle solution for the
adaptive BCI calibration system was
proposed, based on the innovative
recursive algorithm.
There is a constant effort on the
optimization of algorithms. To move
toward multiple degrees of freedom in
humans and in order to improve the BCI
performance the algorithm of fast signal
decomposition were proposed (patent in
preparation).
Leading expert in the field of machine
learning and real time signal processing,
Tetiana AKSENOVA invented several
innovative approaches for signal
processing, classification and modelling
that can be used for Brain Computer
Interface design. She made a crucial
improvement in the theoretical study and
practical application of GMDH-type
(Group Method of Data Handling) neural
networks, effective self learning approach
for the regression analysis which is used
as a basis for self learning procedure of
Brain Computer Interface (BCI).
Her activity at CLINATEC ® overlaps with
the project Neurolink which aims at
improving the stability and the quality of
electrical interface between neural
network and nanostructured electrodes
using multiwall carbon nanotubes.
The challenge of the project is to design
fully autonomous self-paced systems for
continuous long term monitoring of
neuronal activity functioning in natural
noisy environment. Major achievements
have already been obtained:
Functional self-paced BCI with
one degree of freedom in freely moving
animals (rodents) was achieved during
the first year of the project. It includes
the development of basic methods and
algorithms (offline and online), software
implementation on MatLab and their
incorporation into the BCI platform. (A
patent has been submitted in 2010.)
The second year concerned
preclinical studies in animals. Self paced
1D BCI system demonstrated perfect
robustness and high quality of prediction:
the 8 month- long experiment with one
animal validated the robustness of
algorithms. The experiments to study of
Subject-to-Subject variability with
several animals are in progress.
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