Research

Lines of research

Our research team focuses on the application of brain-cognitive machine interfaces for neurofeedback.

Our work falls under three research axes :

The objective of this project is to identify the fundamental mechanisms of learning with feedback (FEEdback LearnIng mechanisms - feeling). By identifying these mechanisms, it is hoped to gain a better understanding of the optimal application conditions for neurofeedback.

The first models of neurofeedback devices date from the 1970s. The objective is to reinforce or correct an altered neurodynamic activity by providing the subject with a direct perception of his brain activity (clues, so that he can obtain reinforcement before even To have succeeded). The idea is similar to that of physiotherapy: one can strengthen a failing muscle by making it work; So a failing cognitive function can be strengthened by making it work. One could compare the task of reinforcing a failing cognitive function with or without feedback to learning to play tennis blindfolded or not. With visual feedback, it is much easier to progress to tennis. Similarly, sensory feedback on brain activity is intended to facilitate the cerebral functioning of the subject for a given cognitive task, while providing a signal of reinforcement as soon as it begins to act in the right direction (ie to Dysfunctional cerebral circuits).

There is, however, very little retreat in the scientific literature concerning the mechanisms of this reinforcement signal. Two arguments are often advanced by detractors of neurofeedback :

    • In the absence of reward, operative conditioning training models do not predict a strengthening of brain mechanisms - on the contrary, one can expect a cognitive load augmented by feedback, and a challenge demotivating to the subject ;
    • Assuming there is a reinforcement mechanism, the subject may be expected to learn the feedback mechanism rather than gaining real control over its cerebral mechanisms. This raises the question of the capacity of generalization of learning under neurofeedback.

Our goal is to measure the correlates (psychological scales, neurophysiological responses) of learning in a task with feedback.

References:

  • Arns M., Batail J.-M., Bioulac S., ongedo M., Daudet C., Drapier D., Fovet T., Jardri R., Le Van Quyen M., Lotte F., Mehler D., Micoulaud Franchi J.-A., Perper-Ouakil D., Vialatte F. Neurofeedback: one of today’s techniques in psychiatry. L’encéphale, S0013-7006(16)30275-5.
  • Gaume A., Vialatte A., Mora-Sánchez A., Ramdani C., Vialatte F.B., A psychoengineering paradigm for the neurocognitive mechanisms of biofeedback and neurofeedback. Neuroscience and Biobehavioral Reviews, 2016, 68:891-910.

The complex mechanisms of the neuronal correlates of cognition have a subjective dimension which is difficult to approach with the usual protocols of this discipline. Consequently, high level functions such as attention, executive functions or emotional cognition are poorly characterized in cognitive neuroscience (they belong to the class known as "hard problems"), Questioning. To overcome the technical limitations of experimental protocols, one would need not to measure, but to interact with the functions in question. One could then go beyond a model based on correlation measures and demonstrate the presence of causal links between the observed responses and the cognitive state of the subject.

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Our hypothesis is that it is possible to control markers of cognitive activities at short time scales (between 0.1 and 10 sec.) With a sufficient reliability by an MCI. By constraining cerebral activity by stimuli, we "mark" functions we are interested in (attention, executive functions, working memory) in order to be able to interact with these functions in real time. The aim of this project is to develop cognitive ICM. We use markers such as evoked steady-state potentials, or responses to cognitive tasks. The team is currently working on sustained visual attention, working memory, and attentional drift.

References:

  • Emge D.K., Vialatte F.B., Dreyfus G., Adalı T., Independent Vector Analysis for SSVEP Signal Enhancement, Detection, and Topographical Mapping. Brain Topography, in press.
  • Mora Sánchez A., Gaume A., Dreyfus G., Vialatte F.B., A Cognitive Brain-Computer Interface Prototype For The Continuous Monitoring Of Visual Working Memory Load. 25th IEEE MLSP international workshop, September 17-20, Boston, USA, 2015.
  • Gaume A., Abbasi M.A., Dreyfus G., Vialatte F.-B., Towards Cognitive BCI: Neural Correlates of Sustained Attention in a Continuous Performance Task. 7th International IEEE/EMBS Conference on Neural Engineering, Montpellier, France, April 22-24 2015.

Numerous signal processing and statistical learning modeling tools exist and are already available to scientists. However, these tools are difficult to use for non-specialists, and remain mainly used in applied mathematics or engineering labs. In many fields, for example in the humanities and social sciences or biology, the study of physiological signals through statistical learning is impossible to carry out due to lack of engineering skills. Laboratories do not have the qualified staff to use these methods. The objective of the Matlab SIGMA box is to provide a teaching utility that is simple to use for modeling applications of these signals (SIGnaux and Modeling by Statistical Learning = SIGMA) for research staff with very limited training in the domain.

Alzheimer’s disease is a devastating and debilitating pathology, affecting not only the memorial functions (its most well-known symptom), but also numerous cognitive functions (social cognition, emotional regulation, executive functions, etc.). Understanding and quantifying the electrophysiological correlates of this disease could allow better management of patients (COMPendrE ALzheimer = compel).

We develop signal processing methods to measure local and long-distance synchronization. These measurements make it possible to study the neuronal correlates of cerebral pathologies, for example for subjects in the early stages of Alzheimer’s disease, or for subjects with dementia (fronto-temporal, vascular-cerebral, Lewy body) . Particular attention is paid to the potential of these tools for neurofeedback applications to promote cognitive reserve in the elderly.

References:

  • Solé-Casals J., Vialatte F.B., Towards Semi-Automatic Artifact Rejection for the Improvement of Alzheimer’s Disease Screening from EEG Signals. Sensors 2015, 15(8):17963-17976.
  • Houmani N., Dreyfus G., Vialatte F.B., Epoch-based entropy for early screening of Alzheimer’s disease, International Journal of Neural Systems, 2015, 25(8):1550032.
  • Gallego-Jutglà E., Solé-Casals J., Vialatte F.B., Elgendi M., Cichocki A., Dauwels J., A hybrid feature selection approach for the early diagnosis of Alzheimer’s disease. Journal of Neural Engineering, 2015, 12(1):016018.
  • Gallego-Jutglà E, Solé-Casals J, Vialatte FB, Dauwels J, Cichocki A. A Theta-Band EEG Based Index for Early Diagnosis of Alzheimer’s Disease. J Alzheimers Dis., 2015, 43(4):1175-84.

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Team

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News

March 2017 : We are looking for a volunteer translator The team is looking for scientists with German as their mother tongue to help us translate (...) 

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Practical information

Unit Director
Thomas Preat
thomas.preat (arobase) espci.fr

Administrator chief
Stéphanie Ledoux
stephanie.ledoux (arobase) espci.fr

Administrator
Tu-Khanh Nguyen
tu-khanh.nguyen (arobase) espci.fr

Phone : +33 (0) 1 40 79 43 02

To contact us