Activity Report 2010 - CNRS

HIGHLIGHT : NANO APPROACHES TO LIFE SCIENCES
CONTACTS
tetiana.aksenova@cea.fr
corinne.mestais@cea.fr
FURTHER READING
Eliseyev et al., LNCS, 6792, 2011 (in press)
IMPLANTABLE
COMPUTER BRAIN
INTERFACE
Up to now, drugs were the main way for
a physician to repair the brain and its
connections to the rest of the body. Deep
brain stimulation has nevertheless
already demonstrated the interest of local
stimulation to correct neural circuit
defects using implantable electrodes.
Nanotechnologies, allowing to record,
stimulate or deliver drugs to neurons
with an unprecedented (unmatched,
unrivaled) resolution will surely help
developing some of the tomorrow’s
neurological treatments. Development of
robust brain-computer interfaces is a key
issue for these progresses.
Development of selflearning
adaptive solutions
for the control of
mechanical effectors
Chair of Excellence 2008: Tetiana
AKSENOVA
Severe motor disabilities require the
development of new communication
pathways to allow the patient controlling
efficiently and safely external aids, such
as wheelchairs and prostheses. The
current method consists in redirecting the
injured nerves into non-essential muscles
and using the electric signals associated
to muscle contraction to monitor the
patient’s intention. The aim of the “Brain-
Computer Interface” project (BCI) is to
directly interpret the brain neural activity
and to translate it into useful command
signals. “Motor signals” are relatively
large in the brain, and can thus be
discriminated from the other neural
activity.
In fact, this work consists in developing
and implementing innovative signal
processing algorithms to analyze
Electrocorticographic signals (ECoG:
electric signals recorded at the surface of
the brain). Animals were instrumented
with ECoG electrodes and trained to
press a pedal to get food at their will,
while ECoG signals were recorded. After
training, a “predictor” was built that could
successfully predict the animal’s intention
(Fig. 1 & 2).
One of the prominent features of this
algorithm is that the success of the
detection is stable for several months
without recalibration, which is very
important for future patient
rehabilitation. Brain computer interface
experiments are now in progress in nonhuman
primates (the step toward human
implantation) and show promising
results.
A
B
Fig. 1: scheme of the brain-computer interface
experiments.
A: training stage, the recorded signals are
used to calibrate the algorithm.
B: the algorithm is used to command the
reward distributor.
Fig. 2: A real-time brain computer interface
experiment. The rat presses the pedal but
decision whether to give a reward is made on
the basis of the recorded ECoG signal.
Although it is still necessary to further
improve the reliability of the detection,
these results successfully demonstrate
that electrocortical electrodes could be
used to control external mechanical
devices and thus rehabilitate paralyzed
people.
Of course, a less invasive system is under
development, consisting in electrodes to
record ECoG signals and circuits to
ensure wireless transmission to the
computer. The first implantation is
scheduled for 2012.
15