2021-04206 – PhD Position F/M : Designing passive hybrid Brain-Computer Interfaces to estimate User eXperience in virtual galleries

About the research center or Inria department

Potioc designs, develops and evaluates new approaches that exploit multimodal interaction to promote a stimulating user experience. In particular, we explore approaches based on mixed reality (AR, RV), tangible interaction, brain-computer interfaces, and physiological interfaces. The main areas of application we are targeting are education, well-being, art, and accessibility.

Context

The hired PhD student will join European project BITSCOPE (2022-2024) – a CHIST-ERA type project which stands for “Brain Integrated Tagging for Socially Curated Online Personalised Experiences”. This is a project led by Pr. Tomas Ward (from Dublin City University, Ireland), in collaboration with France (Inria Bordeaux Sud-Ouest, team Potioc), Spain (Universitat Politècnica de Valencia) and Poland (Nicolas Copernicus University). The BITSCOPE project presents a vision for brain computer interfaces (BCI) which can enhance social relationships in the context of sharing virtual experiences. We envisage a future in which attention, memorability and curiosity elicited in virtual worlds will be measured without the requirement of “likes” and other explicit forms of feedback. Instead, users of our improved BCI technology can explore online experiences leaving behind an invisible trail of neural data-derived signatures of interest. This data, passively collected without interrupting the user, and refined in quality through machine learning, can be used by standard social sharing algorithms such as recommender systems to create better experiences. Technically the work concerns the development of a passive hybrid BCI (phBCI). It is hybrid because it augments electroencephalography (EEG) with eye tracking data, galvanic skin response (GSR), heart rate (HR) and movement in order to better estimate the mental state of the user. It is passive because it operates covertly without distracting the user from their immersion in their online experience and uses this information to adapt the application. It represents a significant improvement in BCI due to the emphasis on improved denoising facilitating operation in home environments and the development of robust classifiers capable of taking inter- and intra-subject variations into account. We leverage our preliminary work in the use of deep learning and geometrical approaches to achieve this improvement in signal quality. The user state classification problem is ambitiously advanced to include recognition of attention, curiosity, and memorability which we will address through advanced machine learning, Riemannian approaches and the collection of large representative datasets in co-designed user centred experiments.

Assignment

As part of this research, the goal of this PhD thesis would be to design the passive hybrid BCI which can be used to create a metric which relates to Users’ eXperience (UX), such as attention, memorability and curiosity with a given artwork. This PhD work will involve protocol design, data collection and experiments in which explicit measures of UX, e.g., self-report will be collected to support supervised learning approaches. Then it will involve the design of machine learning algorithms to estimate such UX (e.g., attention or curiosity levels) from both EEG and physiological signals (e.g., GSR and HR). Finally, it will involve designing an online phBCI to estimate online such states when users are viewing various artworks.

Main activities

It is envisioned that the PhD work will have to solve the following tasks:

  • Designing a controlled protocol to manipulate UX in a virtual exhibition
  • Collecting data with such a controlled UX protocol
  • Designing participant specific EEG-based UX classifiers
  • Designing participant specific physiology-based UX classifiers
  • Building a Multimodal participant specific UX classifier
  • Building a Multimodal generic UX classifier
  • Evaluation and optimization of the proposed UX-BCI classifier in ecological conditions

Skills

  • EEG signal processing (temporal/spatial filtering, subspace identification, source reconstruction, etc)
  • Machine Learning & Pattern Recognition for EEG classification
  • Python / Matlab programming
  • Skills in rigorous protocol design and running, including data collection
  • Able to speak, write and work in an English speaking environment
  • Experience with ElectroEncephaloGraphy (EEG) and/or BCI experiments is a strong plus
  • Experience and/or skills in cognitive science (in particular psychology and/neuroscience) is a strong plus

Benefits package

  • Subsidized meals
  • Partial reimbursement of public transport costs
  • Possibility of teleworking and flexible organization of working hours
  • Professional equipment available (videoconferencing, loan of computer equipment, etc.)
  • Social, cultural and sports events and activities
  • Access to vocational training
  • Social security coverage

Remuneration: 1982€ / month (before taxes) during the first 2 years, 2085€ / month (before taxes) during the third year.

Contract type: Fixed-term contract

Level of qualifications required: Graduate degree or equivalent

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