An approach to extracting graph kernel features from functional brain networks and its applications to the analysis of the noisy EEG signals

Abstract

Since electroencephalographic data (EEG) usually carries a certain amount of noise, it is important to study a method that can propose an effective noise-adaptive feature from EEG signals and can be effectively used for problem-solving. Firstly, to address the problem that the application of noisy EEG in problem-solving based on functional brain networks is significantly worse, we study the extraction of global topological features, called graph kernel features, from functional brain networks with better noise immunity, and propose a method for extracting graph kernel features from networks based on neighborhood subgraph pairwise distances. Secondly, to address the problem of huge data of graph kernel features proposed from functional brain networks, dimensionality reduction of graph kernel features based on kernel principal component analysis is proposed. Finally, to verify that the graph kernel features can not only be effectively used for problem-solving, but also have good noise immunity, the research on fatigue driving and emotion recognition based on the graph kernel feature extraction side of the functional brain network is carried out, and the corresponding fatigue driving state recognition model and emotion state recognition model is constructed. By testing the simulated EEG noisy data on the real fatigue driving dataset and the publicly available emotion recognition dataset Seed with different methods, it is verified that the graph kernel features are effective in classifying the noisy EEG data and have a good generalization ability for different noises.