尽管脑机接口（BCI）系统在多个领域取得了显著进展，但其在性能、可用性及用户满意度等方面仍面临严峻挑战，尤其在智能化水平方面表现明显不足。为此，本文探讨将人工智能（AI）技术，特别是深度学习算法，融入BCI系统，以显著提升其整体性能和智能水平。文章首先回顾BCI的发展历程与原理，系统分析传统了BCI系统在脑信号处理、特征提取和神经解码等关键环节中的局限性。随后，文本深入论述AI技术，尤其是深度学习在建模复杂神经模式、提取高阶特征和增强解码能力方面的潜力与优势。本文重点介绍了AI驱动的BCI在临床应用中的突破性进展，包括辅助语言交流、运动控制重建以及感知反馈增强，并进一步探讨其在非临床场景中的拓展前景。最后，文章还归纳了当前AI-BCI系统在技术实现、伦理规范与社会接受等方面所面临的挑战，提出未来的研究方向，旨在推动构建智能化程度更高、易于普及且用户体验良好的下一代BCI系统，为实现高效的人脑半外部世界交互开辟新的路径。

Despite significant advancements in brain-computer interface (BCI) systems across multiple domains, they continue to face substantial challenges in performance, usability, and user satisfaction—particularly in terms of intelligence. To address these limitations, this paper explores the integration of artificial intelligence (AI) technologies, especially deep learning algorithms, into BCI systems to enhance their overall performance and intelligence levels. The article begins with a review of the development and fundamental principles of BCI, systematically analyzing the limitations of traditional BCI systems in key stages such as brain signal processing, feature extraction, and neural decoding. It then delves into the potential and advantages of AI technologies—particularly deep learning—in modeling complex neural patterns, extracting high-level features, and improving decoding capabilities. Special emphasis is placed on the breakthrough applications of AI-driven BCIs in clinical settings, including aiding speech communication, reconstructing motor control, and enhancing sensory feedback. Furthermore, the paper discusses the expanding potential of AI-BCIs in non-clinical contexts. Finally, it summarizes the current challenges faced by AI-BCI systems in technical implementation, ethical regulation, and societal acceptance, and proposes future research directions. The goal is to foster the development of the next-generation BCI systems that are more intelligent, accessible, and user-friendly, paving the way for more efficient interactions between the human brain and the external world.