Skip to Main content Skip to Navigation

Computational Modeling of User States and Skills for Optimizing BCI Training Tasks

Jelena Mladenovic 1, 2
2 Potioc - Popular interaction with 3d content
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest
Abstract : Brain-Computer Interfaces (BCIs) are systems that enable a person to manipulate an external device with only brain activity, often using ElectroEncephaloGraphgy (EEG). Although there is great medical potential (communication and mobility assistance, as well as neuro-rehabilitation of those who lost motor functions), BCIs are rarely used outside of laboratories. This is mostly due to users’ variability from their brain morphologies to their changeable psychological states, making it impossible to create one system that works with high success for all. The success of a BCI depends tremendously on the user’s ability to focus to give mental commands, and the machine’s ability to decode such mental commands. Most approaches consist in either designing more intuitive and immersive interfaces to assist the users to focus, or enhancing the machine decoding properties. The latest advances in machine decoding are enabling adaptive machines that try to adjust to the changeable EEG during the BCI task. This thesis is unifying the adaptive machine decoding approaches and the interface design through the creation of adaptive and optimal BCI tasks according to user states and traits. Its purpose is to improve the performance and usability of BCIs and enable their use outside of laboratories. To such end, we first created a taxonomy for adaptive BCIs to account for the various changeable factors of the system. Then, we showed that by adapting the task difficulty we can influence a state of flow, i.e., an optimal state of immersion, control and pleasure. which in turn correlates with BCI performance. Furthermore, we have identified the user traits that can benefit from particular types of task difficulties. This way we have prior knowledge that can guide the task adaptation process, specific to each user trait. As we wish to create a generic adaptation rule that works for all users, we use a probabilistic Bayesian model, called Active Inference used in neuroscience to computationally model brain behavior. When we provide such probabilistic model to the machine, it becomes adaptive in such a way that it mimics brain behavior. That way, we can achieve an automatic co-adaptive BCI and potentially get a step closer into using BCIs in our daily lives.
Document type :
Complete list of metadata

Cited literature [284 references]  Display  Hide  Download
Contributor : Abes Star :  Contact
Submitted on : Tuesday, July 7, 2020 - 11:14:05 AM
Last modification on : Friday, July 10, 2020 - 3:10:55 AM
Long-term archiving on: : Friday, November 27, 2020 - 1:03:07 PM


Version validated by the jury (STAR)


  • HAL Id : tel-02891919, version 1



Jelena Mladenovic. Computational Modeling of User States and Skills for Optimizing BCI Training Tasks. Human-Computer Interaction [cs.HC]. Université de Bordeaux, 2019. English. ⟨NNT : 2019BORD0131⟩. ⟨tel-02891919⟩



Record views


Files downloads