基于复杂网络技术的异步脑-机接口分类系统
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TN911.72

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宁夏教育局项目(机电能源装备专业群项目)资助


Brain-Computer Interface Based on Brain Network Techniques
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    摘要:

    脑—机接口(BCI)是一种不依赖周围神经和肌肉组织,通过诱发人脑ERD/ERS等特征信号实现对外部装置自主控制的系统。针对人群中15%到30%的人存在“BCI盲”问题,即难以诱发出较强的ERD/ERS等特征信号,提出将EEG时间序列转换成一个复杂网络,复杂网络的网络测度与大脑意识有关联。结果表明:基于复杂网络构建的PLV二值网络可实现异步BCI系统分类,分类正确率为最高可达88.60%。可见,基于复杂网络技术的异步BCI系统具有可行性,可作为一种新途径。

    Abstract:

    Brain-computer interface (BCI) is a new system which does not rely on the peripheral nervous and muscle tissue, can autonomously control the external devices by inducing human brain ERD/ERS characteristic signals. However, the study found that about 15% to 30% of users existed “BCI blind” problem, which means it is difficult for these users to induce a strong (Event-Related De-synchronization) ERD/(Event-Related Synchronization) ERS signal. This paper are focus on the EEG time series that can be converted into a network whose measures are associated with consciousness, and the results show that PLV binary network can achieve asynchronous BCI system classification.it can be achieved asynchronous BCI system classification, the accuracy is up to 88.60%. It is concluded that the asynchronous BCI system based on brain network technology is feasible and can be used as a new way.

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张微,解承军. 基于复杂网络技术的异步脑-机接口分类系统[J]. 科学技术与工程, 2020, 20(11): 4383-4388.
Zhang Wei, Xie Chengjun. Brain-Computer Interface Based on Brain Network Techniques[J]. Science Technology and Engineering,2020,20(11):4383-4388.

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历史
  • 收稿日期:2019-07-22
  • 最后修改日期:2020-01-04
  • 录用日期:2019-11-05
  • 在线发布日期: 2020-05-29
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