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  • The Inria's Research Teams produce an annual Activity Report presenting their activities and their results of the year. These reports include the team members, the scientific program, the software developed by the team and the new results of the year. The report also describes the grants, contracts and the activities of dissemination and teaching. Finally, the report gives the list of publications of the year.

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Section: New Results

Physiological computing

Participants: Jelena Mladenovic, Fabien Lotte 

 

ElectroGastoGraphy: Recent research in the enteric nervous system, sometimes called the second brain, has revealed potential of the digestive system in predicting emotions. Even though people regularly experience changes in their gastrointestinal (GI) tract which influence their mood and behavior multiple times per day, robust measurements and wearable devices are not quite developed for such phenomena. However, other manifestations of the autonomic nervous system such as electrodermal activity, heart rate, and facial muscle movement have been extensively used as measures of emotions or in biofeedback applications, while neglecting the gut. In [28], we exposed electrogastrography (EGG), i.e., recordings of the myoelectric activity of the GI tract, as a possible measure for inferring human emotions. 

 

EEG-based neuroergonomics: In collaboration with ISAE Toulouse, we explored the use of EEG to monitor cognitive processes in real flight situation, using dry EEG sensors. We showed that doing so is possible, however with low performances, given the strong noise in signals occurring in this challenging context [23]. In general, we presented in [17] and [40] how BCIs could be useful for neuroergonomics, i.e., to estimate user interfaces ergonomics quality from neurophysiological measures. Finally, in collaboration with RIKEN BSI, Japan, we showed that emotions could be monitored to some extent in EEG signals from multiple users watching the same emotional video clips at the same time. Interestingly, emotions decoding performances were increased by using the EEG data from several users compared to using EEG from each individual user [33]