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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications
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EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications

机译：基于EEG的脑电脑界面（BCI）：对信号传感技术和计算智能方法及其应用的最新研究的调查

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摘要

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.
机译：大脑 - 计算机接口（BCIS）增强人脑活动与环境互动的能力。技术和机器学习算法的最新进步增加了对基于脑电图（EEG）的BCI应用的兴趣。基于EEG的智能BCI系统可以促进单调任务下的人类认知状态波动的持续监测，这对于需要医疗保健支持和不同领域的普通研究人员有益。在本文中，我们在BCI应用中调查了最近关于EEG信号传感技术和计算智能方法的文献，弥补了过去五年系统摘要中的差距。具体而言，我们首先介绍BCI的当前状态和用于收集可靠的EEG信号的信号感测技术。然后，我们展示了最先进的计算智能技术，包括模糊模型和在机器学习和深度学习算法中传输学习，以检测，监控和维护普遍应用中的人类认知状态和任务性能。最后，我们提出了一些创新的BCI启发了医疗保健应用，并讨论了基于EEG的BCI研究中的未来研究方向。

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