Anxiety recognition method based on PLI and TD-2DPCA

Considering that anxiety affects people's work and life, accurate detection of anxiety has important application value. Considering the interaction between different channels, a PLI and TD-2DPCA anxiety recognition method based on EEG signal is proposed. In this method, the collected original EEG signal is first preprocessed, the pre-processed signal is decomposed into five rhythmic waves by wavelet change, and the functional connection information of the brain is obtained by phase delay index coupling method for the five signals respectively, and then the anxiety features are extracted by bidirectional and two-dimensional principal component analysis method. Finally, the feature classification is implemented by support vector machine to realize the detection of anxiety. The results show that the proposed method has high recognition accuracy and can identify anxiety information well.