A survey of brain network analysis by electroencephalographic signals

被引:31
|
作者
Luo, Cuihua [1 ,2 ]
Li, Fali [3 ,4 ]
Li, Peiyang [5 ]
Yi, Chanlin [4 ]
Li, Chunbo [4 ]
Tao, Qin [4 ]
Zhang, Xiabing [4 ]
Si, Yajing [6 ]
Yao, Dezhong [3 ,4 ]
Yin, Gang [7 ,8 ]
Song, Pengyun [1 ,2 ]
Wang, Huazhang [1 ,2 ]
Xu, Peng [3 ,4 ]
机构
[1] Southwest Minzu Univ, Sch Elect Engn, Chengdu 610041, Peoples R China
[2] Southwest Minzu Univ, Key Lab Elect Informat State Ethn Affairs Commiss, Chengdu 610041, Peoples R China
[3] Univ Elect Sci & Technol China, Clin Hosp, Chengdu Brain Sci Inst, MOE Key Lab Neuroinformat, Chengdu 611731, Peoples R China
[4] Univ Elect Sci & Technol China, Ctr Informat BioMed, Sch Life Sci & Technol, Chengdu 611731, Peoples R China
[5] Chongqing Univ Post & Telecommun, Sch Bioinformat, Chongqing 400065, Peoples R China
[6] Xinxiang Med Univ, Sch Psychol, Xinxiang 453003, Henan, Peoples R China
[7] Univ Elect Sci & Technol China, Dept Equipment, Sichuan Canc Hosp & Inst, Sichuan Canc Ctr,Sch Med, Chengdu 610054, Peoples R China
[8] Radiat Oncol Key Lab Sichuan Prov, Chengdu 610042, Peoples R China
基金
中国国家自然科学基金;
关键词
Brain network analysis; Segregation and integration; Neuroplasticity; EEG pattern; Artificial intelligence; PSYCHOGENIC NONEPILEPTIC SEIZURES; CONVOLUTIONAL NEURAL-NETWORKS; MILD COGNITIVE IMPAIRMENT; FUNCTIONAL CONNECTIVITY; ALZHEIMERS-DISEASE; GENERATIVE MODELS; INFORMATION-FLOW; WORKING-MEMORY; TOP-DOWN; SOURCE RECONSTRUCTION;
D O I
10.1007/s11571-021-09689-8
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
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.
引用
收藏
页码:17 / 41
页数:25
相关论文
共 50 条
  • [41] Wavelet multiresolution analysis and dyadic scalogram for detection of epileptiform paroxysms in electroencephalographic signals
    Malaver W.J.L.
    Malaver, Wilmer Johan Lobato (wilmer.lobato@gmail.com), 2017, Brazilian Society of Biomedical Engineering (33) : 195 - 201
  • [42] Bispectral pairwise interacting source analysis for identifying systems of cross-frequency interacting brain sources from electroencephalographic or magnetoencephalographic signals
    Chella, Federico
    Pizzella, Vittorio
    Zappasodi, Filippo
    Nolte, Guido
    Marzetti, Laura
    PHYSICAL REVIEW E, 2016, 93 (05)
  • [43] Identification of the brain network controlling systemic inflammatory signals
    Hashimoto, Okito
    Lee, Diana
    Hepler, Tyler
    Tsaava, Tea
    Tynan, Aisling
    Tracey, Kevin J.
    Chavan, Sangeeta S.
    JOURNAL OF IMMUNOLOGY, 2022, 208 (01):
  • [44] Graph Frequency Analysis of Brain Signals
    Huang, Weiyu
    Goldsberry, Leah
    Wymbs, Nicholas F.
    Grafton, Scott T.
    Bassett, Danielle S.
    Ribeiro, Alejandro
    IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2016, 10 (07) : 1189 - 1203
  • [45] Electroencephalographic brain mapping and migraine
    Alberti A.
    Mazzotta G.
    Galletti F.
    Sarchielli P.
    The Journal of Headache and Pain, 2004, 5 (Suppl 2) : S47 - S50
  • [46] A practical method for computing correlation spectra electroencephalographic signals to evaluate functional relationships between brain areas
    Perez, MAG
    Loyo, JR
    Cabrera, MC
    REVISTA MEXICANA DE PSICOLOGIA, 1997, 14 (01): : 5 - 12
  • [47] Parametric Modeling of Band Powers for Electroencephalographic Signals
    Djemili, Rafik
    Belmeguenai, Aissa
    Talbi, Lamine
    10TH INTERNATIONAL CONFERENCE ON SIGNAL-IMAGE TECHNOLOGY AND INTERNET-BASED SYSTEMS SITIS 2014, 2014, : 78 - 83
  • [48] Recognition of Eyelid Movement Using Electroencephalographic Signals
    Chu, Wen-Lin
    Lin, Chih-Jer
    Chen, Ching-Hao
    SENSORS AND MATERIALS, 2020, 32 (01) : 291 - 307
  • [49] On the selection of autoregressive order for electroencephalographic (EEG) signals
    Simpson, DM
    Infantosi, AFC
    Carneiro, JF
    Peixoto, AJ
    Abrantes, LMD
    38TH MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS, PROCEEDINGS, VOLS 1 AND 2, 1996, : 1353 - 1356
  • [50] The Analysis of Brain Signals Using Fuzzy Neural Network In the Diagnosis of Diseases (A case study in Epilepsy)
    Bahrami, Iman
    INTERNATIONAL JOURNAL OF ADVANCED BIOTECHNOLOGY AND RESEARCH, 2016, 7 : 1994 - 2006