Multimodal fusion for anticipating human decision performance.
Sci Rep
; 14(1): 13217, 2024 06 08.
Article
em En
| MEDLINE
| ID: mdl-38851836
ABSTRACT
Anticipating human decisions while performing complex tasks remains a formidable challenge. This study proposes a multimodal machine-learning approach that leverages image features and electroencephalography (EEG) data to predict human response correctness in a demanding visual searching task. Notably, we extract a novel set of image features pertaining to object relationships using the Segment Anything Model (SAM), which enhances prediction accuracy compared to traditional features. Additionally, our approach effectively utilizes a combination of EEG signals and image features to streamline the feature set required for the Random Forest Classifier (RFC) while maintaining high accuracy. The findings of this research hold substantial potential for developing advanced fault alert systems, particularly in critical decision-making environments such as the medical and defence sectors.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Tomada de Decisões
/
Eletroencefalografia
/
Aprendizado de Máquina
Limite:
Adult
/
Female
/
Humans
/
Male
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2024
Tipo de documento:
Article