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典型文献
Automatic Removal of Multiple Artifacts for Single-Channel Electroencephalography
文献摘要:
Removing different types of artifacts from the electroencephalography(EEG)recordings is a critical step in performing EEG signal analysis and diagnosis.Most of the existing algorithms aim for removing single type of artifacts,leading to a complex system if an EEG recording contains different types of artifacts.With the advancement in wearable technologies,it is necessary to develop an energy-efficient algorithm to deal with different types of artifacts for single-channel wearable EEG devices.In this paper,an automatic EEG artifact removal algorithm is proposed that effectively reduces three types of artifacts,i.e.,ocular artifact(OA),transmission-line/harmonic-wave artifact(TA/HA),and muscle artifact(MA),from a single-channel EEG recording.The effectiveness of the proposed algorithm is verified on both simulated noisy EEG signals and real EEG from CHB-MIT dataset.The experimental results show that the proposed algorithm effectively suppresses OA,MA and TA/HA from a single-channel EEG recording as well as physical movement artifact.
文献关键词:
作者姓名:
ZHANG Chenbei;SABOR Nabil;LUO Junwen;PU Yu;WANG Guoxing;LIAN Yong
作者机构:
Department of Micro-Nano Electronics;MoE Key Lab of Artificial Intelligence,Shanghai Jiao Tong University,Shanghai 200240,China;Electrical Engineering Department,Assiut University,Assiut 71516,Egypt;Computing Technology Lab,Alibaba Group,Shanghai 200120,China
引用格式:
[1]ZHANG Chenbei;SABOR Nabil;LUO Junwen;PU Yu;WANG Guoxing;LIAN Yong-.Automatic Removal of Multiple Artifacts for Single-Channel Electroencephalography)[J].上海交通大学学报(英文版),2022(04):437-451
A类:
B类:
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AB值:
0.527118
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