An Identification Recognition Method Based on the Optimization Mechanism of Emotional EEG Module

With the rapid development of informati on society, people's demand for personal privacy a nd property protection has become stronger and st ronger. At present, the traditional biometric techno logy has been difficult to meet the needs of social de velopment. Electroencephalography (EEG), as a un ique biometric feature of individuals, has received wide attention from a large number of researchers. In order to solve the problems of difficult to apply in practice and low recognition accuracy due to the induction of specific situations and differences in i ndividual characteristics in EEG data acquisition, a PSO-Attention-RNN (PARNN) recognition mode 1 is proposed in this paper. Firstly, the energy entro py of five rhythms, a-wave, ß-wave, δ-wave, θ-wave and γ-wave, in EEG signals are extracted as featur e vectors by using wavelet packet transform. These features are then input into the PARNN optimized recognition model, and the EEG temporal frequen cy bands corresponding to different emotional mod ules are filtered using particle swarm optimization (PSO), which can lead to the highest recognition ac curacy for the subjects. The whole process was vali dated in a self-collected emotional EEG database. T he results show that the average recognition accura cy of the algorithm in this paper can reach 90.99%, and the recognition accuracy of the positive emotio n module reaches 93.72%.