Research on the Application of Thinking Neural Signal Recognition and Classification in Personalized Art Computing Library

Electroencephalogram (EEG) is a key resource for the study of brain function, and the interaction between computer and machine through EEG has become a new computer interaction mode. This project intends to study the EEG - computer interaction model based on facial expression. A neural cluster model based on neural network was used to model the mechanism of facial emotions-induced EEG. The EEG activity induced by facial emotion and its spectral characteristics were obtained. In order to improve the recognition accuracy of emotion-driven EEG, the analysis and recognition algorithm of EEG stimulated by expression is studied by combining wavelet with ANN model. The simulation results show that the correct rate of EEG recognition based on neural network is 88.69%. The recognition accuracy of BP neural network is 76.3%, and its convergence speed is also improved. The experimental results show that this method is an effective method for EEG recognition.