[Neuroinfo] Post-doc position in BCI motor mapping in INRIA South France

[Mitsuhiro.Hayashibe at inria.fr]

Postdoctoral position: Adaptive Brain-Motor Mapping

  Neuroprosthetics is an interdisciplinary field related to 
neuroscience, bioelectronics and biomedical engineering, which aims to 
substitute a motor, sensory or cognitive function that might have been 
damaged as a result of an injury or a disease. One of the challenging 
issues in motor prosthesis is the large variety of patient situations 
depending on the type of neurological disorder. To overcome the current 
limited performance of such systems, a robust bio-signal processing and 
a model-based control taking about the actual sensory motor state with 
biosignal feedback would bring a break-through and allow to progress 
toward adaptive neuroprosthesis.
 Recent advances of Brain-Computer-Interfaces (BCI) have opened a new 
communication channel for patients, who can transmit their movement 
intention via brain signals. 
The functionality and controllability of motor prosthesis can be further 
improved by taking advantage of computational mapping between EMG 
(Electromyography) of muscle, EEG (Electroencephalography) of brain, and 
other modalities of biofeedback information.

The first objective is to enhance the classification algorithm to 
extract the subject's motor intention from EEG signal in motor-imagery 
based BCI. The computational modeling between multichannel EMG and EEG 
will involve advanced feature extraction, dimension reduction and 
classification algorithms. Moreover EMG signals of multiple muscles and 
muscle modeling including skeletal dynamics models will help in 
obtaining the detailed motion intention of the subject.

The second objective is to develop a bilateral learning architecture. In 
BCI, adaptive decoding of EEG signals is desirable because brain signals 
change over time during the learning of the task. In motor control, it 
is also known that we change how we use our joints and a way to deal 
with redundancy problems in articulation. In EMG analysis, the change of 
motor usage can be captured. Adaptive modeling of EMG allows the 
evaluation of skill acquisition.

By jointly analyzing both the EEG and EMG modifications, we investigate 
how EEG signal may change along with actual motor coordination changes. 
By modeling both of these adaptive features, this framework will try to 
capture the bilateral learning architecture of both the brain and the 
motor system.

This project is associated with INRIA OpenVibe project (http://openvibe.inria.fr/
)

Required skills: A strong background in signal processing, control, and 
machine learning is required. Fluency in English, and excellent 
programming skills (C++ and Matlab) are necessary.


To start from Sep/Oct 2014, for 1-2 years.

To apply for this position, please contact

Maureen Clerc (http://www-sop.inria.fr/members/Maureen.Clerc/)
Mitsuhiro Hayashibe (http://www.lirmm.fr/~hayashibe/)