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脑机融合控制中脑电伪迹处理方法
Alternative TitleRemoving Artifacts from EEG for Brain-Machine Integration Control
熊馨1; 杨秋红1; 周建华1; 徐保磊2; 李永程2; 尹旭贤2; 伏云发1
Department机器人学研究室
Source Publication昆明理工大学学报(自然科学版)
ISSN1007-855X
2021
Pages1-17
Contribution Rank2
Funding Organization国家自然科学基金项目(82060329,81470084, 81771926, 61463024, 61763022) ; 云南省教育厅项目(2020J0052)
Keyword脑电 伪迹 脑机融合控制 独立成分分析 共同空间模式
Abstract

脑机融合控制的典型控制信号源是脑电,然而脑电信号具有低信噪比、低空间分辨率、极易受到伪迹的污染,这给该类控制系统中脑电信号的处理带来了巨大的挑战。本文针对脑电中存在的各种伪迹,分析总结了各种伪迹处理方法并比较它们的优缺点,最后针对脑机融合控制的实用化需求,指出该领域脑电伪迹处理方法未来的研究方向—在线实时、自适应/机器学习、免伪迹参考、少通道/单通道、优化融合多种有效方法综合去除EEG中的主要伪迹。

Other Abstract

Electroencephalogram (EEG) is the typical control signal source of Brain-Machine Integration Control (BMIC). However, EEG signal’s low signal-to-noise ratio, low spatial resolution and susceptibility to artifact contamination poses great challenge to deal with EEG signal in this kind of control system. In this paper, aiming at various artifacts existing in EEG, the authors analyzed and summarizes the methods of EEG artifacts processing and compared their advantages and disadvantages. Finally, in view of the practical needs of BMIC, the future research direction of EEG artifacts processing methods in this field was we pointed out—online, real time, adaptive/ machine learning, without reference channel, few /single channel, combined algorithm to remove artifacts in EEG signal.

Language中文
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/27987
Collection机器人学研究室
Corresponding Author伏云发
Affiliation1.昆明理工大学信息工程与自动化学院
2.中国科学院沈阳自动化研究所机器人学国家重点实验室
Recommended Citation
GB/T 7714
熊馨,杨秋红,周建华,等. 脑机融合控制中脑电伪迹处理方法[J]. 昆明理工大学学报(自然科学版),2021:1-17.
APA 熊馨.,杨秋红.,周建华.,徐保磊.,李永程.,...&伏云发.(2021).脑机融合控制中脑电伪迹处理方法.昆明理工大学学报(自然科学版),1-17.
MLA 熊馨,et al."脑机融合控制中脑电伪迹处理方法".昆明理工大学学报(自然科学版) (2021):1-17.
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