Characterization of SSMVEP-based EEG signals using multiplex limited penetrable horizontal visibility graph

2019 ◽  
Vol 29 (7) ◽  
pp. 073119 ◽  
Author(s):  
Zhong-Ke Gao ◽  
Wei Guo ◽  
Qing Cai ◽  
Chao Ma ◽  
Yuan-Bo Zhang ◽  
...  
Keyword(s):  
Visibility Graph ◽  
Eeg Signals ◽  
Horizontal Visibility ◽  
Horizontal Visibility Graph

scholarly journals Analysis of alcoholic EEG signals based on horizontal visibility graph entropy

2014 ◽  
Vol 1 (1-4) ◽  
pp. 19-25 ◽  
Author(s):  
Guohun Zhu ◽  
Yan Li ◽  
Peng Wen ◽  
Shuaifang Wang
Keyword(s):  
Visibility Graph ◽  
Eeg Signals ◽  
Horizontal Visibility ◽  
Horizontal Visibility Graph

scholarly journals Multiplex Limited Penetrable Horizontal Visibility Graph from EEG Signals for Driver Fatigue Detection

2019 ◽  
Vol 29 (05) ◽  
pp. 1850057 ◽  
Author(s):  
Qing Cai ◽  
Zhong-Ke Gao ◽  
Yu-Xuan Yang ◽  
Wei-Dong Dang ◽  
Celso Grebogi
Keyword(s):  
Mental Fatigue ◽  
Visibility Graph ◽  
Clustering Coefficient ◽  
Eeg Signals ◽  
Driver Fatigue ◽  
Horizontal Visibility ◽  
Fatigue Driving ◽  
Fatigue Detection ◽  
The Brain ◽  
Horizontal Visibility Graph

Driver fatigue is an important contributor to road accidents, and driver fatigue detection has attracted a great deal of attention on account of its significant importance. Numerous methods have been proposed to fulfill this challenging task, though, the characterization of the fatigue mechanism still, to a large extent, remains to be investigated. To address this problem, we, in this work, develop a novel Multiplex Limited Penetrable Horizontal Visibility Graph (Multiplex LPHVG) method, which allows in not only detecting fatigue driving but also probing into the brain fatigue behavior. Importantly, we use the method to construct brain networks from EEG signals recorded from different subjects performing simulated driving tasks under alert and fatigue driving states. We then employ clustering coefficient, global efficiency and characteristic path length to characterize the topological structure of the networks generated from different brain states. In addition, we combine average edge overlap with the network measures to distinguish alert and mental fatigue states. The high-accurate classification results clearly demonstrate and validate the efficacy of our multiplex LPHVG method for the fatigue detection from EEG signals. Furthermore, our findings show a significant increase of the clustering coefficient as the brain evolves from alert state to mental fatigue state, which yields novel insights into the brain behavior associated with fatigue driving.

scholarly journals Analysing the Dynamics of Interbeat Interval Time Series Using Grouped Horizontal Visibility Graph

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 9926-9934 ◽  
Author(s):  
Gulraiz Iqbal Choudhary ◽  
Wajid Aziz ◽  
Ishtiaq Rasool Khan ◽  
Susanto Rahardja ◽  
Pasi Franti
Keyword(s):  
Time Series ◽  
Visibility Graph ◽  
Interbeat Interval ◽  
Horizontal Visibility ◽  
Interval Time ◽  
Interval Time Series ◽  
Horizontal Visibility Graph

Inference On Streamflow Predictability Using Horizontal Visibility Graph Based Networks

2020 ◽  
Author(s):  
Ganesh Ghimire ◽  
Navid Jadidoleslam ◽  
Witold Krajewski ◽  
Anastasios Tsonis
Keyword(s):  
Time Series ◽  
Time Scale ◽  
Temporal Trends ◽  
Characteristic Exponent ◽  
Visibility Graph ◽  
Clustering Coefficient ◽  
Series Data ◽  
Dynamical Process ◽  
Horizontal Visibility ◽  
Horizontal Visibility Graph

<p>Streamflow is a dynamical process that integrates water movement in space and time within basin boundaries. The authors characterize the dynamics associated with streamflow time series data from about seventy-one U.S. Geological Survey (USGS) stream-gauge stations in the state of Iowa. They employ a novel approach called visibility graph (VG). It uses the concept of mapping time series into complex networks to investigate the time evolutionary behavior of dynamical system. The authors focus on a simple variant of VG algorithm called horizontal visibility graph (HVG). The tracking of dynamics and hence, the predictability of streamflow processes, are carried out by extracting two key pieces of information called characteristic exponent, λ of degree distribution and global clustering coefficient, GC pertaining to HVG derived network. The authors use these two measures to identify whether streamflow process has its origin in random or chaotic processes. They show that the characterization of streamflow dynamics is sensitive to data attributes. Through a systematic and comprehensive analysis, the authors illustrate that streamflow dynamics characterization is sensitive to the normalization, and the time-scale of streamflow time-series. At daily scale, streamflow at all stations used in the analysis, reveals randomness with strong spatial scale (basin size) dependence. This has implications for predictability of streamflow and floods. The authors demonstrate that dynamics transition through potentially chaotic to randomly correlated process as the averaging time-scale increases. Finally, the temporal trends of λ and GC are statistically significant at about 40% of the total number of stations analyzed. Attributing this trend to factors such as changing climate or land use requires further research.</p>

scholarly journals Insights on Streamflow Predictability Across Scales Using Horizontal Visibility Graph Based Networks

2020 ◽  
Vol 2 ◽  
Author(s):  
Ganesh R. Ghimire ◽  
Navid Jadidoleslam ◽  
Witold F. Krajewski ◽  
Anastasios A. Tsonis
Keyword(s):  
Visibility Graph ◽  
Horizontal Visibility ◽  
Horizontal Visibility Graph
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