J Shanghai Jiaotong Univ Sci ›› 2022, Vol. 27 ›› Issue (4): 437-451.doi: 10.1007/s12204-021-2374-5

• Medicine-Engineering Interdisciplinary Research •     Next Articles

Automatic Removal of Multiple Artifacts for Single-Channel Electroencephalography

ZHANG Chenbei1 (张晨贝), SABOR Nabil1,2, LUO Junwen3 (罗竣文), PU Yu3 (蒲 宇), WANG Guoxing1 (王国兴), LIAN Yong1 (连 勇)   

  1. (1. Department of Micro-Nano Electronics; MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai 200240, China; 2. Electrical Engineering Department, Assiut University, Assiut 71516, Egypt; 3. Computing Technology Lab, Alibaba Group, Shanghai 200120, China)
  • Received:2020-08-01 Online:2022-07-28 Published:2022-08-11

Abstract: 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.

Key words: wearable electroencephalography (EEG) devices, ocular artifact (OA), transmission-line/harmonic- wave artifact (TA/HA), muscle artifact (MA), EEG artifacts detection, EEG artifacts removal

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