脑机接口（Brain Computer Interface, BCI）能够在人脑与外部设备之间建立直接的信息交互通路，而无需依赖外周神经与肌肉。经过50余年的发展，BCI研究已从通讯接口设计和自然交互实现阶段，逐渐向脑机智能融合阶段过渡。然而，实现BCI的长时程稳定交互，成为制约其进一步发展和实际应用的关键瓶颈。目前，提升BCI长期稳定性的技术途径主要包括两个方面：一是通过大脑刺激与神经反馈训练等手段增强用户使用BCI的能力，即脑学习；二是利用自适应技术提高机器端对脑信号的解码与适应能力，即机学习。现有多数研究往往单独关注脑学习或机学习，而忽略了二者结合对性能提升的巨大潜力。协同演进BCI则通过有效融合脑学习和机学习过程，使大脑与机器在交互过程中实现相互适应和同步增强，从而克服大脑状态波动和外部环境干扰，支撑BCI的长时程稳定运行。本文系统梳理了神经调制、神经反馈及自适应BCI技术的发展，明确了协同演进BCI的定义与边界，并进一步分析其面临的关键问题与未来发展方向，以期推动协同演进BCI的深入研究与广泛应用。

 Brain-computer interfaces (BCIs) enable direct information exchange between the human brain and external devices without relying on peripheral nerves and muscles. Over the past five decades, BCI research has progressed from the design of communication interfaces and natural interaction to the stage of brain-machine intelligence. However, achieving long-term stable interaction remains a major bottleneck limiting further development and practical application of BCI systems. Current approaches to improve long-term stability mainly fall into two categories: enhancing the user’s ability to operate the BCI through neural modulation and neurofeedback training, referred to as brain learning; and improving the machine’s decoding and adaptation to brain signals through adaptive algorithms, referred to as machine learning. Most existing studies tend to focus on either brain learning or machine learning in isolation, overlooking the potential of their integration for performance enhancement. Co-evolution BCI aims to synergise brain learning and machine learning, enabling mutual adaptation and synchronised enhancement between the brain and machine during interaction. This facilitates robust performance against brain state fluctuations and environmental disturbances, thereby supporting long-term stable BCI operation. This paper provides a comprehensive review of the development of neural modulation, neurofeedback, and adaptive BCI technologies, defines the concept and scope of co-evolution BCI, and analyses the key challenges and future directions to promote deeper research and broader application of co-evolution BCIs