EEG-based brain functional connectivity representation using amplitude locking value for fatigue-driving recognition

It has been shown that brain functional networks constructed from electroencephalographic signals (EEG) continuously change topology as brain fatigue increases, and extracting the topological properties of the network can characterize the degree of brain fatigue. However, the traditional brain function network construction process often selects only the amplitude or phase components of the signal to measure the relationship between brain regions, and the use of a single component of the signal to construct a brain function network for analysis is rather one-sided. Therefore, we propose a method of functional synchronization analysis of brain regions. This method takes the EEG signal based on empirical modal decomposition (EMD) to obtain multiple intrinsic modal components (IMF) and inputs them into the Hilbert transform to obtain the instantaneous amplitude, and then calculates the amplitude locking value (ALV) to measure the synchronization relationship between all pairs of channels. The topological properties of the brain functional network are extracted to classify awake and fatigue states. The brain functional network is constructed based on the adjacency matrix of each waveform obtained from the ALV between all pairs of channels to realize the synchronization analysis between brain regions. Moreover, we achieved a satisfactory classification accuracy (82.84%) using the discriminative connection features in the Alpha band. In this study, we analyzed the functional network of ALV brain in fatigue and awake state, and the results showed that the connections between brain regions in fatigue state were significantly increased, and the connections between brain regions in the awake state were significantly decreased, and the information interaction between brain regions was more orderly and efficient.