Enhancing Brain-Computer Interface Performance and Security through Advanced Artificial Intelligence Techniques

The brain-computer interface has become a rapidly developing field, but it has also brought many problems with its development. The main issues are the sparse amount of brain-computer interface data, the inaccurate decoding and classification of data, and the data security of the brain-computer interface. With the development of artificial intelligence, artificial intelligence also provides solutions to many problems. This study mainly uses artificial intelligence algorithms to solve these problems. This paper reviews the integration of artificial intelligence techniquesspecifically transfer learning, generative adversarial networks (GANs), Transformer models, and federated learningto address critical challenges in brain-computer interfaces (BCIs), including data scarcity, classification accuracy, and data security. The hybrid model has many outstanding performances in solving the brain-computer interface problem, and this paper mainly mentions the joint extraction of spatiotemporal features of the CNN-Transformer to make up for the shortcomings of a single model and improve the overall performance. The GAN-TL hybrid model can effectively reduce the influence of individual differences on the model. This paper illustrates the advantages of the hybrid model, which is also the main direction of future research. It highlights how hybrid AI models significantly enhance BCI performance while outlining current limitations and future research directions to ensure robust, efficient, and secure BCI applications.