Department of Electronics and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China.
Stud Health Technol Inform. 2023 Nov 23;308:295-302. doi: 10.3233/SHTI230853.
The application of artificial intelligence (AI) algorithms is an indispensable portion of developing brain-computer interfaces (BCI). With the continuous development of AI concepts and related technologies. AI algorithms such as neural networks play an increasingly powerful and extensive role in brain-computer interfaces. However, brain-computer interfaces are still facing many technical challenges. Due to the limitations of AI algorithms, brain-computer interfaces not only work with limited accuracy, but also can only be applied to certain simple scenarios. In order to explore the future directions and improvements of AI algorithms in the area of brain-computer interfaces, this paper will review and analyse the advanced applications of AI algorithms in the field of brain-computer interfaces in recent years and give possible future enhancements and development directions for the controversial parts of them. This review first presents the effects of different AI algorithms in BCI applications. A multi-objective classification method is compared with evolutionary algorithms in feature extraction of data. Then, a kind of supervised learning algorithm based on Event Related Potential (ERP) tags is presented to achieve a high accuracy in the process of pattern recognition. Finally, as an important experimental paradigm for BCI, a combined TFD-PSR-CSP feature extraction method, is explained for the problem of motor imagery. The "Discussion" part comprehensively analyses the advantages and disadvantages of the above algorithms and proposes a deep learning-based artificial intelligence algorithm in order to solve the problems arising from the above algorithms.
人工智能 (AI) 算法的应用是开发脑机接口 (BCI) 的不可或缺的一部分。随着人工智能概念和相关技术的不断发展，神经网络等人工智能算法在脑机接口中发挥着越来越强大和广泛的作用。然而，脑机接口仍面临许多技术挑战。由于人工智能算法的局限性，脑机接口不仅准确性有限，而且只能应用于某些简单的场景。为了探索人工智能算法在脑机接口领域的未来方向和改进，本文将回顾和分析近年来人工智能算法在脑机接口领域的先进应用，并对有争议的部分给出可能的未来增强和发展方向。本文综述首先介绍了不同人工智能算法在 BCI 应用中的作用。一种多目标分类方法与进化算法在数据特征提取方面进行了比较。然后，提出了一种基于事件相关电位 (ERP) 标记的监督学习算法，以实现模式识别过程中的高精度。最后，作为 BCI 的一个重要实验范例，针对运动想象问题，解释了一种联合 TFD-PSR-CSP 特征提取方法。“讨论”部分全面分析了上述算法的优缺点，并提出了一种基于深度学习的人工智能算法，以解决上述算法中出现的问题。
