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基于脑磁图的智能脑机交互关键技术

[Key technologies for intelligent brain-computer interaction based on magnetoencephalography].

作者信息

Xu Haotian, Gong Anmin, Ding Peng, Luo Jiangong, Chen Chao, Fu Yunfa

机构信息

Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China.

Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P. R. China.

出版信息

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2022 Feb 25;39(1):198-206. doi: 10.7507/1001-5515.202108069.

Abstract

Brain-computer interaction (BCI) is a transformative human-computer interaction, which aims to bypass the peripheral nerve and muscle system and directly convert the perception, imagery or thinking activities of cranial nerves into actions for further improving the quality of human life. Magnetoencephalogram (MEG) measures the magnetic field generated by the electrical activity of neurons. It has the unique advantages of non-contact measurement, high temporal and spatial resolution, and convenient preparation. It is a new BCI driving signal. MEG-BCI research has important brain science significance and potential application value. So far, few documents have elaborated the key technical issues involved in MEG-BCI. Therefore, this paper focuses on the key technologies of MEG-BCI, and details the signal acquisition technology involved in the practical MEG-BCI system, the design of the MEG-BCI experimental paradigm, the MEG signal analysis and decoding key technology, MEG-BCI neurofeedback technology and its intelligent method. Finally, this paper also discusses the existing problems and future development trends of MEG-BCI. It is hoped that this paper will provide more useful ideas for MEG-BCI innovation research.

摘要

脑机接口(BCI)是一种变革性的人机交互方式,旨在绕过外周神经和肌肉系统,直接将脑神经的感知、想象或思维活动转化为行动,以进一步提高人类生活质量。脑磁图(MEG)测量神经元电活动产生的磁场。它具有非接触测量、高时空分辨率和准备方便等独特优势。它是一种新型的BCI驱动信号。MEG-BCI研究具有重要的脑科学意义和潜在应用价值。到目前为止,很少有文献阐述MEG-BCI所涉及的关键技术问题。因此,本文聚焦于MEG-BCI的关键技术,详细介绍了实际MEG-BCI系统中涉及的信号采集技术、MEG-BCI实验范式的设计、MEG信号分析与解码关键技术、MEG-BCI神经反馈技术及其智能方法。最后,本文还讨论了MEG-BCI存在的问题和未来发展趋势。希望本文能为MEG-BCI创新研究提供更多有益的思路。

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