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BrainPredict: a Tool for Predicting and Visualising Local Brain Activity

Abstract : In this paper, we present a tool allowing dynamic prediction and visualization of an individual's local brain activity during a conversation. The prediction module of this tool is based on classifiers trained using a corpus of human-human and human-robot conversations including fMRI recordings. More precisely, the module takes as input behavioral features computed from raw data, mainly the participant and the interlocutor speech but also the participant's visual input and eye movements. The visualisation module shows in real-time the dynamics of brain active areas synchronised with the behavioral raw data. In addition, it shows which integrated behavioral features are used to predict the activity in individual brain areas.
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Submitted on : Tuesday, May 19, 2020 - 4:02:37 PM
Last modification on : Sunday, June 26, 2022 - 10:19:41 AM


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  • HAL Id : hal-02612819, version 1


Youssef Hmamouche, Laurent Prevot, Magalie Ochs, Chaminade Thierry. BrainPredict: a Tool for Predicting and Visualising Local Brain Activity. Proceedings of The 12th Language Resources and Evaluation Conference, Nov 2020, Marseille, France. pp.703-709. ⟨hal-02612819⟩



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