Emo-FilM: A multimodal dataset for affective neuroscience using naturalistic stimuli

The Emo-FilM dataset stands for Emotion research using Films and fMRI in healthy participants. This dataset includes emotion annotations by 44 raters for 14 short films with a combined duration of over 2½ hours and recordings of respiration, heart rate, and functional magnetic resonance imaging (fMRI) from a sample of 30 individuals watching the same films. 50 items were annotated including discrete emotions and emotion components from the domains of appraisal, motivation, motor expression, physiological response, and feeling. The ratings had a mean inter-rater agreement of 0.38. The fMRI data acquired at 3 Tesla is includes high-resolution structural and resting state fMRI for each participant. Physiological recordings included heart rate, respiration, and electrodermal activity. This dataset is designed, but not limited, to studying the dynamic neural processes involved in emotion experience. It has a high temporal resolution of annotations, and includes validations of annotations by the fMRI sample. The Emo-FilM dataset is a treasure trove for researching emotion in response to naturalistic stimulation in a multimodal framework.

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Publication Details

Journal
Scientific Data
Published
2025-04-23
DOI
https://doi.org/10.1038/s41597-025-04803-5
Citations
6
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
8.59
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article

Emo-FilM: A multimodal dataset for affective neuroscience using naturalistic stimuli

Dimitri Van De Ville, Raphaël Fournier, Michał Muszyński, Laura Vilaclara et al.
6 citations
Scientific Data
Functional Brain Connectivity Studies
8.59
article

Emo-FilM: A multimodal dataset for affective neuroscience using naturalistic stimuli

Dimitri Van De Ville, Raphaël Fournier, Michał Muszyński, Laura Vilaclara, Patrik Vuilleumier, Elenor Morgenroth, Stefano Moia, Maria Ploumitsakou, Marina Almató-Bellavista
article en
6 citations

Abstract

The Emo-FilM dataset stands for Emotion research using Films and fMRI in healthy participants. This dataset includes emotion annotations by 44 raters for 14 short films with a combined duration of over 2½ hours and recordings of respiration, heart rate, and functional magnetic resonance imaging (fMRI) from a sample of 30 individuals watching the same films. 50 items were annotated including discrete emotions and emotion components from the domains of appraisal, motivation, motor expression, physiological response, and feeling. The ratings had a mean inter-rater agreement of 0.38. The fMRI data acquired at 3 Tesla is includes high-resolution structural and resting state fMRI for each participant. Physiological recordings included heart rate, respiration, and electrodermal activity. This dataset is designed, but not limited, to studying the dynamic neural processes involved in emotion experience. It has a high temporal resolution of annotations, and includes validations of annotations by the fMRI sample. The Emo-FilM dataset is a treasure trove for researching emotion in response to naturalistic stimulation in a multimodal framework.

Scientific DataVol. 12(1)
University of Geneva (CH), University Hospital of Geneva (CH), Centre d'Imagerie BioMedicale (CH), École Polytechnique Fédérale de Lausanne (CH), Geneva College (US)
Openalex Percentile: Top 2%
Functional Brain Connectivity Studies
8.59
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Emo-FilM: A multimodal dataset for affective neuroscience using naturalistic stimuli — Dimitri Van De Ville, Raphaël Fournier, et al. · Scientific Data (2025) | TGRS Research Map | TGRS