Research on Deep Learning Model for Real-time Data Processing of Brain-Computer Interface System

In the field of brain-computer interface (BCI), by accurately capturing and analyzing brain neural signals, building a bridge of direct communication between people and digital devices, it has shown great application potential and prospects. Despite this, efficient processing of real-time data is still regarded as one of the key problems that need to be solved in this field. In view of this, this paper innovatively proposes an efficient real-time data processing architecture based on deep learning technology, the core of which is an advanced model based on variational autoencoder (VAE). The uniqueness of this model is that it can cleverly handle the inherent nonlinear and high-dimensional characteristics of EEG signals, thereby significantly improving the decoding accuracy and data processing efficiency of the signal. The algorithm in this paper cleverly combines deep neural networks to optimize the signal extraction process and feature selection strategy. The performance of the proposed model is comprehensively evaluated through carefully designed simulation experiments. The experimental results convincingly prove that this model greatly improves the decoding accuracy of brain-computer interface signals while ensuring the processing speed. Specifically, data analysis shows that compared with traditional methods, this model performs well in average classification accuracy, data processing speed and anti-interference ability, achieving significant transcendence. In summary, the deep learning framework proposed in this paper not only demonstrates excellent robustness and scalability, but also opens a new path for the real-time application exploration of brain-computer interface technology, and provides strong theoretical support and practical guidance for the optimization and upgrading of future brain-computer interface systems.