title: EEG-based research on brain functional networks in cognition.
abstract: Recently, exploring the cognitive functions of the brain by establishing a network model to understand the working mechanism of the brain has become a popular research topic in the field of neuroscience. In this study, electroencephalography (EEG) was used to collect data from subjects given four different mathematical cognitive tasks: recite numbers clockwise and counter-clockwise, and letters clockwise and counter-clockwise to build a complex brain function network (BFN). By studying the connectivity features and parameters of those brain functional networks, it was found that the average clustering coefficient is much larger than its corresponding random network and the average shortest path length is similar to the corresponding random networks, which clearly shows the characteristics of the small-world network. The brain regions stimulated during the experiment are consistent with traditional cognitive science regarding learning, memory, comprehension, and other rational judgment results. The new method of complex networking involves studying the mathematical cognitive process of reciting, providing an effective research foundation for exploring the relationship between brain cognition and human learning skills and memory. This could help detect memory deficits early in young and mentally handicapped children, and help scientists understand the causes of cognitive brain disorders.
authors.affiliation: School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, 12 East Qing he xiao ying RD, Hai dian Dist, Beijing 100192, China.
authors.forename: Niannian
authors.initials: N
authors.lastname: Wang
authors.affiliation: School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, 12 East Qing he xiao ying RD, Hai dian Dist, Beijing 100192, China.
authors.forename: Li
authors.initials: L
authors.lastname: Zhang
authors.affiliation: School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, 12 East Qing he xiao ying RD, Hai dian Dist, Beijing 100192, China.
authors.forename: Guozhong
authors.initials: G
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D003071:Cognition / Q000502:physiology*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D055641:Mathematical Concepts; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D011340:Problem Solving / Q000502:physiology*; D012107:Research Design
keywords: EEG; brain function network; cognitive memory; small-word property