This Post-doc position is conducted in the context of the HEMISFER collaborative research project, which aims at designing novels concepts of « NeuroFeedback » in the context of rehabilitation related to motor deficiencies or psychiatric disorders. The main objective will be the design of new therapeutic tools based on innovative technologies of Virtual Reality and Neurofeedback (NF). The major expected breakthrough will come from the design and use of a coupling model associating realtime functional and metabolic information from Magnetic Resonance Imaging (rtfMRI) to Electroencephalography (EEG) to “enhance” NF protocols.
Neurofeedback consists in presenting to the patient a feedback related to his/her brain activity, in order for him/her to progressively learn how to control it. This progressive improvement in controlling such brain activities is expected to improve the rehabilitation and treatment of pathologies, which are known to be associated with them. Neurofeedback thus combines: 1) real-time brain activity acquisition systems such as EEG (electroencephalography) and 2) display systems for providing feedback to the subjects such as with monitor screens and visual gauges. In this Post-doc program, we will search for innovative paths on both sides, i.e., 1) the exploitation of a novel combination of several cerebral recording devices (real-time EEG *plus* fMRI devices), and 2) the exploitation of novel feedback strategies based on multi-sensory feedback and virtual reality technologies.
The objective of the post-doc will be to adapt a joint EEG/fMRI coupling model, to develop methods able to learn the respective sensors parameters given the observations performed during simultaneous fMRI and EEG acquisitions, under NF stimulation paradigms, and to exploit the trained model when the stimulation paradigms are performed with EEG only. In such cases, the learned coupling model will “supplement” the EEG NF signals when recorded alone (typically through NF sessions in clinical services). Based on this, the post-doc will study the design of novel paradigms of Neurofeedback under visual or auditory stimuli that can be conducted with fMRI coupled with EEG recordings. The targeted pathologies are related to functional rehabilitation (stroke) and psychiatric disorders (depressions). The post- doc will have the possibility to investigate these aspects according to the actual applicant experience. Evaluations will be conducted in close collaborations with medical doctors and Prof. I. BONAN (Visages U746, Rennes Hospital CHU) and Prof. D. DRAPIER (EA 4712, Rennes psychiatric hospital CHGR).
This work will be conducted at Inria under the HEMISFER project of the Labex “CominLabs”. It will form collaboration between the Unit/Project VISAGES U746 (INSERM/INRIA/CNRS/university of Rennes I), and the PANAMA and HYBRID Teams at Inria Rennes. This work will benefit from a new research 3T MRI systems provided by the NeurInfo in-vivo neuroimaging platform on which these new research protocols will be set up (http://www.neurinfo.org).
Deadline for application: May 31th, 2016
Research teams: HYBRID, PANAMA and VISAGES Teams, Inria Rennes (http://www.irisa.fr/)
Associate Supervisors:
- C. BARILLOT (+33 2 99847505 / Christian.Barillot@irisa.fr)
- A. LECUYER (+33 2 99847483 / Anatole.Lecuyer@inria.fr)
- R. GRIBONVAL (+33 2 99842506 / Remi.Gribonval@inria.fr)
Keywords: Signal and Image Analysis, Brain Computer Interfaces, Sparse Representation, Non linear estimation, Clinical Neurosciences, Medical Imaging,
Profile
This position requires background in applied mathematics, numerical analysis, and statistics as well as in signal and image processing. A good practice on computer sciences, especially in Matlab and in object-oriented programming (C++) will be appreciated.
References
1. J. F. Lubar, M. O. Swartwood, J. N. Swartwood, and P. H. O’Donnell, “Evaluation of the effectiveness of EEG neurofeedback training for ADHD in a clinical setting as measured by changes in T.O.V.A. scores, behavioral ratings, and WISC-R performance,” Biofeedback Self Regul, vol. 20, pp. 83-99, Mar 1995.
2. Lecuyer, F. Lotte, R. B. Reilly, R. Leeb, M. Hirose, and M. Slater, “Brain-Computer Interfaces, Virtual Reality, and Videogames,” Computer, vol. 41, pp. 66-72, 2008.
3. R. Leeb, D. Friedman, G. R. Muller-Putz, R. Scherer, M. Slater, and G. Pfurtscheller, “Selfpaced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic,” Comput Intell Neurosci, p. 79642, 2007.
4. J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Braincomputer interfaces for communication and control,” Clin Neurophysiol, vol. 113, pp. 767-91, Jun 2002.
5. F. Bach, R. Jenatton, J. Mairal, and G. Obozinski, “Optimization with Sparsity-Inducing Penalties,” Foundations and Trends in Machine Learning, vol. 4, no. 1. pp. 1–106, 2012.
6. S. Nam, M. E. Davies, M. Elad, and R. Gribonval, “The cosparse analysis model and algorithms,” Appl. Comp. Harm. Anal., vol. 34, no. 1, pp. 30–56, 2013.
7. Michael Lustig, David Donoho, and John M. Pauly, Sparse MRI: The application of compressed sensing for rapid MR imaging. (Magnetic Resonance in Medicine, 58(6) pp. 1182 – 1195, December 2007)
8. L. Yu, P. Maurel, C. Barillot, R. Gribonval, Compressive Matched Filter for Cerebral Blood Flow Quantification with ASL: sampling diversity or repetition? MICCAI Workshop on Sparsity Techniques in Medical Imaging, 2012
9. Thomas Oberlin, Christian Barillot, Rémi Gribonval, Pierre Maurel. Symmetrical EEG-FMRI Imaging by Sparse Regularization. EUSIPCO – 23rd European Signal Processing Conference, Aug 2015, Nice, France. pp. 1-5, 2015.
