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

Weijie Liu

doi:10.54254/2755-2721/2025.TJ23002

Research Article

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Published on 15 May 2025

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Liu,W. (2025). Enhancing Brain-Computer Interface Performance and Security through Advanced Artificial Intelligence Techniques. Applied and Computational Engineering,154,1-6.

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

Weijie Liu *^,1,

¹ Guangdong University of Technology, Guangdong, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/2025.TJ23002

Abstract

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 techniques—specifically transfer learning, generative adversarial networks (GANs), Transformer models, and federated learning—to 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.

Keywords

brain-computer interfaces, Convolutional neural networks, Generate adversarial networks, Federated Learning, Transformer

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Cite this article

Liu,W. (2025). Enhancing Brain-Computer Interface Performance and Security through Advanced Artificial Intelligence Techniques. Applied and Computational Engineering,154,1-6.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of CONF-SEML 2025 Symposium: Machine Learning Theory and Applications

Conference website: https://2025.confseml.org/tianjin.html

ISBN：978-1-80590-117-4(Print) / 978-1-80590-118-1(Online)

Conference date: 18 May 2025

Editor：Hui-Rang Hou

Series: Applied and Computational Engineering

Volume number: Vol.154

ISSN：2755-2721(Print) / 2755-273X(Online)

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