Optimal Lead Research on Bispectral Features of Driving Fatigue EEG Signal

Inhalt:
By extracting the bispectral slice features of the driving fatigue EEG signal, the optimal reference lead of the bispectral feature index of driving fatigue is studied, which provides theoretical support for the future development of driving fatigue detection equipment and reducing vehicle traffic accidents. First, the EEG signals were extracted from the simulated driving experiment of 12 volunteers at different time periods, and then the bispectral characteristic EEG topographic maps were drawn by extracting the eigenvalues of driving fatigue EEG bispectral slices to analyze the changes of the characteristic bispectral eigenvalues of each brain area. In the end, LIBSVM classifier are used to classify 19-lead bispectral slice feature indexes, and compare the difference of feature values of different leads and the classification effect. The results show that the FP2 lead in the forehead area has a high classification accuracy rate of 82.83%, and the FP2 lead can be used as an important reference lead for studying the bispectral characteristics of driving fatigue.