Zhang Meihong, Qian Bocheng, Gao Jianming, Zhao Shaokai, Cui Yibo, Luo Zhiguo, Shi Kecheng, Yin Erwei
School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China.
Sensors (Basel). 2025 Aug 21;25(16):5215. doi: 10.3390/s25165215.
As brain-computer interface (BCI) technology continues to advance, research on human brain function has gradually transitioned from theoretical investigation to practical engineering applications. To support EEG signal acquisition in a variety of real-world scenarios, BCI electrode systems must demonstrate a balanced combination of electrical performance, wearing comfort, and portability. Dry electrodes have emerged as a promising alternative for EEG acquisition due to their ability to operate without conductive gel or complex skin preparation. This paper reviews the latest progress in dry electrode EEG systems, summarizing key achievements in hardware design with a focus on structural innovation and material development. It also examines application advances in several representative BCI domains, including emotion recognition, fatigue and drowsiness detection, motor imagery, and steady-state visual evoked potentials, while analyzing system-level performance. Finally, the paper critically assesses existing challenges and identifies critical future research priorities. Key recommendations include developing a standardized evaluation framework to bolster research reliability, enhancing generalization performance, and fostering coordinated hardware-algorithm optimization. These steps are crucial for advancing the practical implementation of these technologies across diverse scenarios. With this survey, we aim to offer a comprehensive reference and roadmap for researchers engaged in the development and implementation of next-generation dry electrode EEG-based BCI systems.
随着脑机接口（BCI）技术的不断进步，对人类脑功能的研究已逐渐从理论研究转向实际工程应用。为了支持在各种现实场景中采集脑电图（EEG）信号，BCI电极系统必须在电气性能、佩戴舒适度和便携性之间实现平衡。由于无需导电凝胶或复杂的皮肤预处理即可运行，干电极已成为EEG采集的一种有前途的替代方案。本文综述了干电极EEG系统的最新进展，总结了硬件设计方面的关键成果，重点关注结构创新和材料开发。还研究了几个具有代表性的BCI领域的应用进展，包括情绪识别、疲劳和困倦检测、运动想象以及稳态视觉诱发电位，同时分析了系统级性能。最后，本文批判性地评估了现有挑战，并确定了未来的关键研究重点。主要建议包括制定标准化评估框架以提高研究可靠性、增强泛化性能以及促进硬件 - 算法协同优化。这些步骤对于推动这些技术在不同场景中的实际应用至关重要。通过本次综述，我们旨在为从事下一代基于干电极EEG的BCI系统开发和实施的研究人员提供全面的参考和路线图。
