Interpretable prediction of brain activity during conversations from multimodal behavioral signals.

ABSTRACT: We present an analytical framework aimed at predicting the local brain activity in uncontrolled experimental conditions based on multimodal recordings of participants' behavior, and its application to a corpus of participants having conversations with another human or a conversational humanoid robot. The framework consists in extracting high-level features from the raw behavioral recordings and applying a dynamic prediction of binarized fMRI-recorded local brain activity using these behavioral features. The objective is to identify behavioral features required for this prediction, and their relative weights, depending on the brain area under investigation and the experimental condition. In order to validate our framework, we use a corpus of uncontrolled conversations of participants with a

SUBMITTER: Hmamouche Y 

PROVIDER: S-EPMC10956754 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature