Decoding Mental Effort in a Quasi-Realistic Scenario: A Feasibility Study on Multimodal Data Fusion and Classification.
Sensors (Basel)
; 23(14)2023 Jul 20.
Article
em En
| MEDLINE
| ID: mdl-37514840
ABSTRACT
Humans' performance varies due to the mental resources that are available to successfully pursue a task. To monitor users' current cognitive resources in naturalistic scenarios, it is essential to not only measure demands induced by the task itself but also consider situational and environmental influences. We conducted a multimodal study with 18 participants (nine female, M = 25.9 with SD = 3.8 years). In this study, we recorded respiratory, ocular, cardiac, and brain activity using functional near-infrared spectroscopy (fNIRS) while participants performed an adapted version of the warship commander task with concurrent emotional speech distraction. We tested the feasibility of decoding the experienced mental effort with a multimodal machine learning architecture. The architecture comprised feature engineering, model optimisation, and model selection to combine multimodal measurements in a cross-subject classification. Our approach reduces possible overfitting and reliably distinguishes two different levels of mental effort. These findings contribute to the prediction of different states of mental effort and pave the way toward generalised state monitoring across individuals in realistic applications.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Reserva Cognitiva
Tipo de estudo:
Prognostic_studies
Limite:
Adult
/
Female
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Humans
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Male
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article