A Multimodal Feature Fusion Brain Fatigue Recognition System Based on Bayes-gcForest.
Sensors (Basel)
; 24(9)2024 May 02.
Article
en En
| MEDLINE
| ID: mdl-38733015
ABSTRACT
Modern society increasingly recognizes brain fatigue as a critical factor affecting human health and productivity. This study introduces a novel, portable, cost-effective, and user-friendly system for real-time collection, monitoring, and analysis of physiological signals aimed at enhancing the precision and efficiency of brain fatigue recognition and broadening its application scope. Utilizing raw physiological data, this study constructed a compact dataset that incorporated EEG and ECG data from 20 subjects to index fatigue characteristics. By employing a Bayesian-optimized multi-granularity cascade forest (Bayes-gcForest) for fatigue state recognition, this study achieved recognition rates of 95.71% and 96.13% on the DROZY public dataset and constructed dataset, respectively. These results highlight the effectiveness of the multi-modal feature fusion model in brain fatigue recognition, providing a viable solution for cost-effective and efficient fatigue monitoring. Furthermore, this approach offers theoretical support for designing rest systems for researchers.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Teorema de Bayes
/
Electroencefalografía
Límite:
Adult
/
Female
/
Humans
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Male
Idioma:
En
Revista:
Sensors (Basel)
Año:
2024
Tipo del documento:
Article
País de afiliación:
China