Eye-tracking and Electrodermal activity real-time measurements for attention analysis during neurofeedback training

Publié le jeu 06/01/2022 - 17:08
Type de contrat
Equipe de recherche
Project infos:
This 2-years postdoctoral position is part of an Exploratory Action funded by the research national institut Inria. This means we are exploring original ideas to address difficult challenges.

You will be working in the Empenn research group, one of the very few lab in the world, equipped with the bi-modal EEG-fMRI neurofeedback technology, at the Neurinfo platform (located at the University Hospital of Rennes).

This position lies at the interface of signal processing, behavioural neuroscience and neurofeedback.

Neurofeedback approaches (NF), also known as restorative brain-computer interface (restorative BCI), consist in providing real-time feedback to a subject or patient about his or her own brain activity in order to self-regulate brain areas or networks, targeted by the neural rehabilitation or by a given task.

The estimation of neurofeedback scores is done through online brain functional feature extraction relying for the majority on electroencephalography (EEG) or functional magnetic resonance imaging (fMRI) and some very recent ones employing both for bi-modal EEG-fMRI NF sessions (i.e., NF scores are estimated synchronously by features from both modalities), providing a more specific estimation of the underlying neural activity.

NF is a very promising brain rehabilitation technique for psychiatric disorders, stroke and other neurological pathologies, yet with moderate results.

One central question in NF training, is to identify the origin of a failure which can be due to the signal recording and artefacts, a too difficult task, the patient’s inability to learn via NF, or a lack of attention from the participant during the task (he/she should be preserved from boredom to not disengage from the task). Also, motivation should be enhanced, and participant's prevailing attention should be monitored.

Eye-tracking (ET) and skin conductance (SC) devices are used in behavioural neuroscience to measure different aspects of patient’s mental states related to focus, arousal, mind wandering, mental load or anxiety. All key indicators for a precise assessment of patient’s motivation.

Electro-dermal activity (EDA), measured via SC system, detects changes in the conductivity of the skin owing to perspiration. ET is a technology that measures eye movements at a high spatio-temporal resolution.


To analyse participant’s motivation, the recruited postdoctoral fellow will integrate measure from eye-tracking and EDA signals and help the research engineer on the set-up and data acquisition at the Neurinfo platform (University of Rennes).

The main objective of this position is to determine robust features of ET and EDA signals to characterise each of the mental states of interest (i.e. mind wandering, mental load, arousal and focus) that cannot be described with a single parameter. The temporal delays (around 4-5 seconds) between the stimuli and EDA (and ET) signals are not a problem for a use with EEG-fMRI recording, as the fMRI signal also accounts for a haemodynamic response delay of few seconds (depending on the brain location).

The methodological work will be two steps:

  • Identifying measures, robust across subjects, for motivation evaluation and changes detection.
  • Modelling the impact of motivation measures on EEG and fMRI signals, and on EEG-fMRI signal coherence.

Data will be acquired during the postdoctoral stay to support methodological developments. 2 publications are expected: one with the research engineer on the new platform recording EEG, fMRI, ET and EDA signals, and one on the methodological developments.

For more details, please contact claire [*] curyatinria [*] fr and antoine [*] coutrotatinsa-lyon [*] fr


The recruited person will be collaborating with Dr Antoine Coutrot (CNRS), expert in eye-tracking and behaviour.

The recruited person will be closely working with the research engineer to set-up the platform recording EEG, fMRI, ET and EDA signals.

Some references:

[1]  I. P. Bodala, J. Li, N. V. Thakor, and H. Al-Nashash. EEG and Eye Tracking De- monstrate Vigilance Enhancement with Challenge Integration. Frontiers in Human Neuroscience, 10, 2016. Publisher : Frontiers.

[2]  W. Boucsein. Electrodermal Activity. Springer Science & Business Media, Feb. 2012.

[3]  D. Clewett, C. Gasser, and L. Davachi. Pupil-linked arousal signals track the temporal organization of events in memory. Nature Communications, 11(1) :4007, Aug. 2020. Number : 1 Publisher : Nature Publishing Group.

[4]  C. Cury, G. Lioi, L. Perronnet, A. Lécuyer, P. Maurel, and C. Barillot. Impact of 1D and 2D Visualisation on EEG-fMRI Neurofeedback Training During a Motor Imagery Task. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pages 1018–1021, Apr. 2020. ISSN : 1945-8452.

[5]  T. Kootstra, J. Teuwen, J. Goudsmit, T. Nijboer, M. Dodd, and S. V. d. Stigchel. Machine learning-based classification of viewing behavior using a wide range of sta- tistical oculomotor features. Journal of Vision, 20(9) :1–1, Sept. 2020. Publisher : The Association for Research in Vision and Ophthalmology.

[6]  F. Nijboer, N. Birbaumer, and A. Kübler. The influence of psychological state and motivation on brain-computer interface performance in patients with amyotrophic lateral sclerosis - a longitudinal study. Frontiers in Neuroscience, 2010.

[7]  I. M. Pavisic, N. C. Firth, S. Parsons, D. M. Rego, T. J. Shakespeare, K. X. X. Yong, C. F. Slattery, R. W. Paterson, A. J. M. Foulkes, K. Macpherson, A. M. Car- ton, D. C. Alexander, J. Shawe-Taylor, N. C. Fox, J. M. Schott, S. J. Crutch, and S. Primativo. Eyetracking Metrics in Young Onset Alzheimers Disease : A Win- dow into Cognitive Visual Functions. Frontiers in Neurology, 8, 2017. Publisher : Frontiers.

[8]  R. Sitaram, T. Ros, L. Stoeckel, S. Haller, F. Scharnowski, J. Lewis-Peacock, N. Weiskopf, M. L. Blefari, M. Rana, E. Oblak, N. Birbaumer, and J. Sulzer. Closed-loop brain training : the science of neurofeedback. Nature Reviews Neu- roscience, 18(2) :86–100, Feb. 2017.

[9]  J. Smallwood and J. W. Schooler. The Science of Mind Wandering : Empiri- cally Navigating the Stream of Consciousness. Annual Review of Psychology, 66(1) :487–518, 2015.

[10]  B. Sorger, F. Scharnowski, D. E. Linden, M. Hampson, and K. D. Young. Control freaks : Towards optimal selection of control conditions for fMRI neurofeedback studies. NeuroImage, 186 :256–265, Feb. 2019.

Main activities

  • Propose and develop new real-time metric for ET and EDA signals
  • Propose signal processing of ET and EDA signals
  • Develop method to analyse ET and EDA metric with EEG and fMRI data
  • Design experimental protocol to reveal potential of ET and EDA measures during EEG-fMRI neurofeedback training and fMRI task sessions
  • Write scientific publications
  • Present the works and results in international conference
Profil / Compétences

Technical skills :
Signal processing, knowledges
Eye-tracking, substantial experience
Skin conductance / EDA, substantial experience
Computer Science (programming Matlab, python or C++, machine learning or modelling), substantial experience
Protocol design on healthy subjects for fMRI task or neurofeedback sessions, some experience, knowledges or at least be interested in it
Neuro-imaging: knowledges in EEG, fMRI is a plus
Neuroscience, general knowledge and interest

Languages :
Good English communications skills (listening, verbal, written)
Some French communications skills (listening, verbal, written), for eventual non-english communication.

Relational skills :
Managing Multiple Priorities
Interpersonal Abilities
Durée du contrat (en mois)
Lieu de travail
Diplôme requis
PhD in Neuroscience
Date limite de candidature

We are looking for a researcher with experience in EDA and/or eye-tracking signals analysis. The applicant must be autonomous enough to conduct exploratory research, and be pro-active to find original solutions. Knowledges in EEG and fMRI data is a plus.

The candidate should also have experience in signal processing, machine learning and/or modelling, and programming skills (Python, Matlab or C++).

It is also essential that the candidate presents good communication skills, as he/she will have to interact with researchers and engineers presenting various expertise, located on different sites.

Your application must contain an updated CV, a motivation letter including your professional perspectives and the presentation of your favourite own publication, reports from the reviewers of your PhD thesis, a link toward the PDF of your PhD thesis, 2 reference letters.

Application here : https://jobs.inria.fr/public/classic/en/offres/2021-03930