Research on classification of brain functional network features during mental fatigue / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 171-175, 2018.
Artigo
em Chinês
| WPRIM
| ID: wpr-687649
ABSTRACT
This study is aimed to investigate objective indicators of mental fatigue evaluation to improve the accuracy of mental fatigue evaluation. Mental fatigue was induced by a sustained cognitive task. The brain functional networks in two states (normal state and mental fatigue state) were constructed based on electroencephalogram (EEG) data. This study used complex network theory to calculate and analyze nodal characteristics parameters (degree, betweenness centrality, clustering coefficient and average path length of node), and served them as the classification features of support vector machine (SVM). Parameters of the SVM model were optimized by gird search based on 6-fold cross validation. Then, the subjects were classified. The results show that characteristic parameters of node of brain function networks can be divided into normal state and mental fatigue state, which can be used in the objective evaluation of mental fatigue state.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Tipo de estudo:
Estudo prognóstico
Idioma:
Chinês
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2018
Tipo de documento:
Artigo
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