Activity Report 2010 - CNRS

Implantable brain computer
interface
Chair of Excellence 2008: Tetiana
AKSENOVA
Coordinator: Corinne MESTAIS (Léti).
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’s neural activity and to
translate it into useful command signals.
This project therefore relies on the
development of nanostructured
microelectrodes for peri- or intra-cranial
neuron recording and stimulation - one of
the goals of Clinatec ® . Furthermore,
“motor signals” are relatively large in the
brain, and can thus be discriminated from
the other neural activity.
Fig. 9: Scheme of the brain-computer interface
experiments.
Top: training stage, the recorded signals are
used to calibrate the Iterative N-way Partial
Least Squares projection algorithm.
Bottom: the algorithm is used to command the
reward distributor.
SCIENTIFIC REPORT
In fact, this work consists in developing
and implementing innovative signal
processing algorithms to analyze
Electrocorticographic signals (ECoG:
electric signals recorded at the brain
surface). Rats were trained to press a
pedal to get food at their will and ECoG
signals were recorded. A first set of rats
was used to build a “predictor” of the
animal’s intention. The algorithm was
then implemented in real time as an
order to control the food reward
independently of the pedal position (Fig.
9).
The results obtained are quite
impressive, and clearly demonstrate that
it is possible to monitor the animal
intentions in this way. Also, the detection
is stable for several months without
recalibration, which is very important for
future patient rehabilitation (Fig. 10).
This Brain Computer Interface system is
currently applied to primates to control a
motorized arm (Fig. 11).
Please read the corresponding Highlight
at the end of this report for further
information
Fig. 10: Plot of the recorded observation points
as a function of the first and second principal
components of the predictor, at the beginning
(left) and at the end (right) of the experiment.
Fig. 11: 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.
The success of this stage is essential to
strongly demonstrate that electrocortical
electrodes could be used in human to
control external mechanical devices and
thus rehabilitate paralyzed people. For
this purpose, it is necessary to further
improve the reliability of the detection.
FURTHER READING:
Neural Computation, 21, 2648–2666,
(2009)
Filtering out of Artifacts of Deep Brain
Stimulation Using Nonlinear
Oscillatory Model
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