Utilisation de l'analyse par vision par ordinateur pour l'annotation des périodes d'inattention dans les enregistrements EEG avec des stimuli visuels

Titre original en anglais : Use of computer vision analysis for labeling inattention periods in EEG recordings with visual stimuli

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Isaev, D., Major, S., Carpenter, K. L. H., Grapel, J., Chang, Z., Martino, J. M. D., Carlson, D., Dawson, G., & Sapiro, G. (2025). Use of computer vision analysis for labeling inattention periods in EEG recordings with visual stimuli. Scientific Reports, 15(1), 30963-30963. https://doi.org/10.1038/s41598-025-10511-2

Date de publication: 22/08/2025 Ajout dans AutiHub: 05/07/2026 Type: Article Langue de l’article: Anglais
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Auteurs

Auteur·ices des publications
9
Auteur·ices de la publication identifié·es comme autistes
1 / 9 (11,1 %)
  1. Dmitry Isaev
  2. Samantha Major
  3. Kimberly L. H. Carpenter
  4. Jordan Grapel *
  5. Zhuoqing Chang
  1. J. Matías Di Martino
  2. David Carlson
  3. Géraldine Dawson
  4. Guillermo Sapiro

Résumé

Les enregistrements d'électroencéphalographie (EEG) avec des stimuli visuels nécessitent un codage détaillé afin de déterminer les périodes d'attention du participant. Nous proposons ici d'utiliser uniquement un modèle d'apprentissage automatique supervisé et des caméras vidéo disponibles dans le commerce. Nous extrayons de la vidéo du participant des caractéristiques fondées sur la vision par ordinateur, telles que la pose de la tête, le regard et les repères du visage, et entraînons le modèle d'apprentissage automatique (perceptron multicouche) sur un jeu de données initial, puis l'adaptons à l'aide d'un petit sous-ensemble de données provenant d'un nouveau participant. En utilisant un échantillon de 23 enfants autistes avec et sans TDAH concomitant (trouble du déficit de l'attention avec hyperactivité), âgés de 49 à 95 mois, et en entraînant le modèle sur 2560 images annotées supplémentaires (équivalant à 85,3 s de la vidéo) d'un nouveau participant, la médiane de l'aire sous la courbe caractéristique de fonctionnement du récepteur pour la détection de l'inattention était de 0,989 (IQR 0,984-0,993) et la fiabilité inter-évaluateurs médiane (kappa de Cohen) avec un annotateur humain formé était de 0,888. L'accord avec les annotations humaines pour neuf participants se situait dans la plage de 0,616 à 0,944. Nos résultats démontrent la faisabilité d'outils automatiques pour détecter l'inattention pendant les enregistrements EEG, ainsi que leur potentiel pour réduire la subjectivité et la charge de temps du codage humain de l'attention. L'outil d'adaptation du modèle et de visualisation des caractéristiques de vision par ordinateur est mis à la disposition publique de la communauté de recherche.

Electroencephalography (EEG) recordings with visual stimuli require detailed coding to determine the periods of participant's attention. Here we propose to use a supervised machine learning model and off-the-shelf video cameras only. We extract computer vision-based features such as head pose, gaze, and face landmarks from the video of the participant, and train the machine learning model (multi-layer perceptron) on an initial dataset, then adapt it with a small subset of data from a new participant. Using a sample size of 23 autistic children with and without co-occurring ADHD (attention-deficit/hyperactivity disorder) aged 49-95 months, and training on additional 2560 labeled frames (equivalent to 85.3 s of the video) of a new participant, the median area under the receiver operating characteristic curve for inattention detection was 0.989 (IQR 0.984-0.993) and the median inter-rater reliability (Cohen's kappa) with a trained human annotator was 0.888. Agreement with human annotations for nine participants was in the 0.616-0.944 range. Our results demonstrate the feasibility of automatic tools to detect inattention during EEG recordings, and its potential to reduce the subjectivity and time burden of human attention coding. The tool for model adaptation and visualization of the computer vision features is made publicly available to the research community.

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Ces indicateurs décrivent la bibliographie citée importée pour cette publication. Les métriques de références citées utilisent le total des références citées comme dénominateur. Les métriques d’auteurices cité·es indiquent si elles utilisent toutes les occurrences d’auteurices cité·es ou seulement les occurrences rattachées à des auteurices déjà intégré·es à la base de données AutiHub. Ils utilisent les rattachements mis en cache entre les auteurices cité·es et les auteurices intégré·es à la base de données AutiHub. Dernier calcul : 16/08/2026 11:30.

Références citées
47
Nombre total de références citées intégrées pour cette publication.
Références citées avec un·e auteur·ice identifié·e comme autiste
0 / 47 (0,0 %)
Occurrences d’auteur·ices cité·es identifié·es comme autistes
0 / 324 (0,0 %)
Parmi les occurrences rattachées à des auteurices intégré·es à la base de données AutiHub : 0 / 69 (0,0 %). Auteurices cité·es distinct·es identifié·es comme autistes : 0 / 261 (0,0 %).
Occurrences citées rattachées à la base AutiHub
69 / 324 (21,3 %)
Auteurices cité·es distinct·es rattaché·es : 26 / 261 (10,0 %)
Occurrences rattachées, non identifiées comme autistes
69 / 69 (100,0 %)
Parmi les seules occurrences rattachées. Sur l’ensemble des occurrences d’auteurices cité·es : 69 / 324 (21,3 %). Auteurices cité·es distinct·es rattaché·es, non identifié·es comme autistes : 26 / 26 (100,0 %).
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