PMID- 32581918
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20200928
IS  - 1664-1078 (Print)
IS  - 1664-1078 (Electronic)
IS  - 1664-1078 (Linking)
VI  - 11
DP  - 2020
TI  - Brain Network Constancy and Participant Recognition: an Integrated Approach to 
      Big Data and Complex Network Analysis.
PG  - 1003
LID - 10.3389/fpsyg.2020.01003 [doi]
LID - 1003
AB  - With the development of big data sharing and data standardization, 
      electroencephalogram (EEG) data are increasingly used in the exploration of human 
      cognitive behavior. Most of the existing studies focus on the changes of human 
      brain network topology (the number of connections, degree distribution, 
      clustering coefficient phantom) in various cognitive behaviors. However, there 
      has been little exploration into the steady state of multi-cognitive behaviors 
      and the recognition of multi-participant brain networks. To solve these two 
      problems, we used EEG data of 99 healthy participants from the PhysioBank to 
      study multi-cognitive behaviors. Specifically, we calculated the symbolic 
      transfer entropy (STE) between 64 electrode sequences of EEG data and constructed 
      the brain networks of various cognitive behaviors of each participant using the 
      directed minimum spanning tree (DMST) algorithm. We then investigated the 
      eigenvalue spectrum of the STE matrix of each individual's cognitive behavior. 
      The results also showed that the spectrum distributions of different cognitive 
      states of the same participant remained relatively stable, but those of the same 
      cognitive state of different participants varied considerably, verifying the 
      relative stability and uniqueness of the human brain network similar to a human's 
      fingerprint. Based on these features, we used the spectral distribution set of 99 
      participants of various cognitive states as the original data set and developed a 
      spectral distribution set scoring (SDSS) method to identify the brain network 
      participants. It was found that most labels (69.35%) of the test participant with 
      the highest score were identical to the labeled participant. This study provided 
      further evidence for the existence of human brain fingerprints, and furnished a 
      new approach for dynamic identification of brain fingerprints.
CI  - Copyright (c) 2020 Qiu and Nan.
FAU - Qiu, Lu
AU  - Qiu L
AD  - School of Finance and Business, Shanghai Normal University, Shanghai, China.
AD  - Department of Finance, East China University of Science and Technology, Shanghai, 
      China.
FAU - Nan, Wenya
AU  - Nan W
AD  - Department of Psychology, College of Education, Shanghai Normal University, 
      Shanghai, China.
LA  - eng
PT  - Journal Article
DEP - 20200603
PL  - Switzerland
TA  - Front Psychol
JT  - Frontiers in psychology
JID - 101550902
PMC - PMC7283910
OTO - NOTNLM
OT  - brain network constancy
OT  - complex network
OT  - directed minimum spanning tree (DMST)
OT  - participant recognition
OT  - symbolic transfer entropy (STE)
EDAT- 2020/06/26 06:00
MHDA- 2020/06/26 06:01
PMCR- 2020/06/03
CRDT- 2020/06/26 06:00
PHST- 2019/04/07 00:00 [received]
PHST- 2020/04/22 00:00 [accepted]
PHST- 2020/06/26 06:00 [entrez]
PHST- 2020/06/26 06:00 [pubmed]
PHST- 2020/06/26 06:01 [medline]
PHST- 2020/06/03 00:00 [pmc-release]
AID - 10.3389/fpsyg.2020.01003 [doi]
PST - epublish
SO  - Front Psychol. 2020 Jun 3;11:1003. doi: 10.3389/fpsyg.2020.01003. eCollection 
      2020.