@inproceedings{hmamouche-etal-2020-brainpredict,
title = "{B}rain{P}redict: a Tool for Predicting and Visualising Local Brain Activity",
author = "Hmamouche, Youssef and
Pr{\'e}vot, Laurent and
Ochs, Magalie and
Chaminade, Thierry",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.89/",
pages = "710--716",
language = "eng",
ISBN = "979-10-95546-34-4",
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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<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.</abstract>
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%0 Conference Proceedings
%T BrainPredict: a Tool for Predicting and Visualising Local Brain Activity
%A Hmamouche, Youssef
%A Prévot, Laurent
%A Ochs, Magalie
%A Chaminade, Thierry
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G eng
%F hmamouche-etal-2020-brainpredict
%X 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.
%U https://aclanthology.org/2020.lrec-1.89/
%P 710-716
Markdown (Informal)
[BrainPredict: a Tool for Predicting and Visualising Local Brain Activity](https://aclanthology.org/2020.lrec-1.89/) (Hmamouche et al., LREC 2020)
ACL