Recognition and Analysis of Motor Imagery EEG Signal Based on Improved BP Neural Network

With the rapid development of neuroinformatics and related intelligent algorithms, the research of recognition and classification based on EEG signals is becoming more and more important and valuable. With the progress of science and technology, the related research of EEG signal recognition and processing has been gradually applied to rehabilitation medicine, intelligent information processing, and other cross-cutting fields. As one of the most important research directions in the field of the brain–computer interface, motor imagery EEG has a wide range of applications. At the same time, it shows a good application effect in the process of application practice. At present, the main EEG recognition and analysis algorithms always have some problems and defects in data processing, such as low signal-to-noise ratio, unclean noise filtering, and high data dimension. In this paper, based on the improved BP neural network algorithm, weight splitting technology is added to the traditional BP neural network algorithm. In order to solve the filtering problem, this paper uses the non-linear mapping function of the traditional BP neural network, and intelligently trains the small weight particles by combining the particle swarm filter algorithm, so as to improve the filtering performance of the whole BP algorithm. Based on the above two algorithms, the problem of low signal-to-noise ratio (SNR) and unclean filtering in EEG data processing caused by fast weight degradation in traditional BP algorithm can be solved. Finally, according to the actual data of brain–computer interface, this paper compares the improved BP neural network algorithm with the traditional BP neural network algorithm in recognition and analysis of motor imagery EEG signals. The experiment shows that the proposed algorithm has obvious advantages in recognition accuracy and analysis effect.