Open AccessPhase Fluctuation Analysis in Functional Brain Networks of Scaling EEG for Driver Fatigue Detection
Author(s) -
Rongrong Fu,
Mengmeng Han,
Bao Yu,
Peiming Shi,
Jiangtao Wen
Publication year - 2020
Publication title -
promet
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.315
H-Index - 19
eISSN - 1848-4069
pISSN - 0353-5320
DOI - 10.7307/ptt.v32i4.3303
Subject(s) - electroencephalography , pattern recognition (psychology) , scaling , eeg fmri , computer science , artificial intelligence , characterization (materials science) , phase (matter) , psychology , neuroscience , mathematics , physics , geometry , quantum mechanics , optics
The characterization of complex patterns arising from electroencephalogram (EEG) is an important problem with significant applications in identifying different mental states. Based on the operational EEG of drivers, a method is proposed to characterize and distinguish different EEG patterns. The EEG measurements from seven professional taxi drivers were collected under different states. The phase characterization method was used to calculate the instantaneous phase from the EEG measurements. Then, the optimization of drivers’ EEG was realized through performing common spatial pattern analysis. The structures and scaling components of the brain networks from optimized EEG measurements are sensitive to the EEG patterns. The effectiveness of the method is demonstrated, and its applicability is articulated.