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A survey of brain network analysis by electroencephalographic signals.
Luo, Cuihua; Li, Fali; Li, Peiyang; Yi, Chanlin; Li, Chunbo; Tao, Qin; Zhang, Xiabing; Si, Yajing; Yao, Dezhong; Yin, Gang; Song, Pengyun; Wang, Huazhang; Xu, Peng.
Afiliación
  • Luo C; School of Electrical Engineering, Southwest Minzu University, Chengdu, 610041 China.
  • Li F; Key Laboratory of Electronic Information of State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, 610041 China.
  • Li P; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731 China.
  • Yi C; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China.
  • Li C; School of Bioinformatics, Chongqing University of Post and Telecommunications, Chongqing, 400065 China.
  • Tao Q; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China.
  • Zhang X; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China.
  • Si Y; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China.
  • Yao D; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China.
  • Yin G; School of Psychology, Xinxiang Medical University, Xinxiang, 453003 China.
  • Song P; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731 China.
  • Wang H; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China.
  • Xu P; Department of Equipment, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610054 China.
Cogn Neurodyn ; 16(1): 17-41, 2022 Feb.
Article en En | MEDLINE | ID: mdl-35126769
Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Cogn Neurodyn Año: 2022 Tipo del documento: Article Pais de publicación: Países Bajos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Cogn Neurodyn Año: 2022 Tipo del documento: Article Pais de publicación: Países Bajos