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
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)
Received:
2020-08-01
Online:
2022-07-28
Published:
2022-08-11
CLC Number:
ZHANG Chenbei (张晨贝), SABOR Nabil, LUO Junwen (罗竣文), PU Yu (蒲 宇), WANG Guoxing (王国兴), LIAN Yong∗ (连 勇). Automatic Removal of Multiple Artifacts for Single-Channel Electroencephalography[J]. J Shanghai Jiaotong Univ Sci, 2022, 27(4): 437-451.
[1] | SABOR N, LI Y, ZHANG Z, et al. Detection of the interictal epileptic discharges based on wavelet bispectrum interaction and recurrent neural network [J]. Science China Information Sciences, 2021, 64(6): 162403. |
[2] | ZHANG Q, XIE Q, DUAN K, et al. A digital signal processor (DSP)-based system for embedded continuous-time cuffless blood pressure monitoring using single-channel PPG signal [J]. Science China Information Sciences, 2020, 63(4): 1-3. |
[3] | ZHAO Q, HU B, SHI Y, et al. Automatic identification and removal of ocular artifacts in EEG—improved adaptive predictor filtering for portable applications [J]. IEEE Transactions on NanoBioscience,2014, 13(2): 109-117. |
[4] | SCHL ? G L A , K E I N R A T H C , Z I M M E R M A N N D , e t al. A fully automated correction method of EOG artifacts in EEG recordings [J]. Clinical Neurophysiology,2007, 118(1): 98-104. |
[5] | JUNG T P, MAKEIG S, HUMPHRIES C, et al.Removing electroencephalographic artifacts by blind source separation [J]. Psychophysiology, 2000, 37(2):163-178. |
[6] | GHANDEHARION H, AHMADI-NOUBARI H. Detection and removal of ocular artifacts using Independent Component Analysis and wavelets [C]//2009 4th International IEEE/EMBS Conference on Neural Engineering. Antalya: IEEE, 2009: 653-656. |
[7] | CASTELLANOS N P, MAKAROV V A. Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis [J]. Journal of Neuroscience Methods, 2006, 158(2): 300-312. |
[8] | SAINI M, PAYAL, SATIJA U. An effective and robust framework for ocular artifact removal from single-channel EEG signal based on variational mode decomposition [J]. IEEE Sensors Journal, 2020, 20(1):369-376. |
[9] | KHATUN S, MAHAJAN R, MORSHED B I. Comparative study of wavelet-based unsupervised ocular artifact removal techniques for single-channel EEG data [J]. IEEE Journal of Translational Engineering in Health and Medicine, 2016, 4: 1 - 8 . |
[10] | KHATUN S, MAHAJAN R, MORSHED B I. Comparative analysis of wavelet based approaches for reliable removal of ocular artifacts from single channel EEG [C]//2015 IEEE International Conference on Electro/Information Technology(EIT ). Dekalb, IL:IEEE, 2015: 335-340. |
[11] | HE P, WILSON G, RUSSELL C. Removal of ocular artifacts from electro-encephalogram by adaptive filtering [J]. Medical and Biological Engineering and Computing, 2004, 42(3): 407-412. |
[12] | PARADESHI K P, KOLEKAR U D. Ocular artifact suppression in multichannel EEG using dynamic segmentation and enhanced wICA [J]. IETE Journal of Research, 2020: 1-14. |
[13] | PARADESHI K P, SCHOLAR R, KOLEKAR U D.Removal of ocular artifacts from multichannel EEG signal using wavelet enhanced ICA [C]//2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). Chennai:IEEE, 2017: 383-387. |
[14] | NAM H, YIM T G, HAN S K, et al. Independent component analysis of ictal EEG in medial temporal lobeepilepsy [J]. Epilepsia, 2002, 43(2): 160-164. |
[15] | URRESTARAZU E, IRIARTE J, ALEGRE M, et al.Independent component analysis removing artifacts inictal recordings [J]. Epilepsia, 2004, 45(9): 1071-1078. |
[16] | DIMIGEN O. Optimizing the ICA-based removal of ocular EEG artifacts from free viewing experiments[J]. NeuroImage, 2020, 207: 116117. |
[17] | JANANI A S, GRUMMETT T S, BAKHSHAYESH H,et al. How many channels are enough evaluation of tonic cranial muscle artefact reduction using ICA with different numbers of EEG channels [C]//2018 26th European Signal Processing Conference (EUSIPCO). |
Rome: IEEE, 2018: 101-105. | |
[18] | ACHARYYA A, JADHA V P N, BONO V, et al. Low-complexity hardware design methodology for reliable and automated removal of ocular and muscular artifact from EEG [J]. Computer Methods and Programs in Biomedicine, 2018, 158: 123-133. |
[19] | BAI Y, W AN X, ZENG K, et al. Reduction hybrid arti-facts of EMG–EOG in electroencephalography evokedby prefrontal transcranial magnetic stimulation [J].Journal of Neural Engineering, 2016, 13(6): 066016. |
[20] | CHEN X, LIU A, CHIANG J, et al. Removing mus-cle artifacts from EEG data: Multichannel or single-channel techniques? [J]. IEEE Sensors Journal, 2016,16(7): 1986-1997. |
[21] | X U X , L I U A , C H E N X . A n o v e l f e w - c h a n n e l s t r a t e g yfor removing muscle artifacts from multichannel EEGdata [C]//2017 IEEE Global Conference on Signal andInformation Processing (GlobalSIP). Montreal, QC:IEEE, 2017: 976-980. |
[22] | DE CLERCQ W, VERGULT A, V ANRUMSTE B, etal. Canonical correlation analysis applied to removemuscle artifacts from the electroencephalogram [J].IEEE Transactions on Biomedical Engineering, 2006,53(12): 2583-2587. |
[23] | GAO J, ZHENG C, W ANG P. Online removal of mus-cle artifact from electroencephalogram signals basedon canonical correlation analysis [J]. Clinical EEG andNeuroscience, 2010, 41(1): 53-59. |
[24] | MAMMONE N, LA FORESTA F, MORABITO F C.Automatic artifact rejection from multichannel scalpEEG by wavelet ICA [J]. IEEE Sensors Journal, 2012,12(3): 533-542. |
[25] | MIJOVI ′C B, DE VOS M, GLIGORIJEVI ′C I , e t a l .Source separation from single-channel recordings by combining empirical-mode decomposition and inde-pendent component analysis [J]. IEEE Transactions onBiomedical Engineering, 2010, 57(9): 2188-2196. |
[26] | CHEN X, CHEN Q, ZHANG Y, et al. A novel EEMD-CCA approach to removing muscle artifacts for per-vasive EEG [J]. IEEE Sensors Journal, 2019, 19(19):8420-8431. |
[27] | L I U Y , Z H O U Y , L A N G X , e t a l . A n e fficient androbust muscle artifact removal method for few-channelEEG [J]. IEEE Access, 2019, 7: 176036-176050. |
[28] | TORRES M E, COLOMINAS M A, SCHLOT-THAUER G, et al. A complete ensemble empiricalmode decomposition with adaptive noise [C]//2011IEEE International Conference on Acoustics, Speechand Signal Processing (ICASSP). Prague: IEEE, 2011:4144-4147. |
[29] | GOLDBERGER A L, AMARAL L A, GLASS L, etal. PhysioBank, PhysioToolkit, and PhysioNet: Com-ponents of a new research resource for complex phys-iologic signals [J]. Circulation, 2000, 101(23): E215-E220. |
[30] | SHOEB A H. Application of machine learning toepileptic seizure onset detection and treatment[D].Cambridge: Massachusetts Institute of Technology,2009. |
[31] | GAO J F, YANG Y, LIN P, et al. Automatic removalof eye-movement and blink artifacts from EEG signals[J]. Brain Topography, 2010, 23(1): 105-114. |
[32] | HUANG N E, SHEN Z, LONG S R, et al. The em-pirical mode decomposition and the Hilbert spectrumfor nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London SeriesA: Mathematical, Physical and Engineering Sciences,1998, 454(1971): 903-995. |
[1] | Fu Zeyu, Fu Zhuang, Guan Yisheng. Vascular Interventional Surgery Path Planning and 3D Visual Navigation [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 472-481. |
[2] | Feng Lingdong, Miao Yubin. Intelligent Heart Rate Extraction Method Based on Millimeter Wave Radar [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 493-498. |
[3] | Duan Jizhong, Xu Yuhán, Huang Huan. Fast Parallel Magnetic Resonance Imaging Reconstruction Based on Sparsifying Transform Learning and Structured Low-Rank Model [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 499-509. |
[4] | Si Bingqi, Pang Chenxi, Wang Zhiwu, Jiang Pingping, Yan Guozheng. Real-Time Lightweight Convolutional Neural Network for Polyp Detection in Endoscope Images [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 521-534. |
[5] | Ma Ting, Wu Jianfang, Hu Feng, Nie Wei, Liu Youxin. Image Mosaic Method of Capsule Endoscopy Intestinal Wall Based on Improved Weighted Fusion [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 535-544. |
[6] | Duan Jizhong, Su Yan. Improved Sensitivity Encoding Parallel Magnetic Resonance Imaging Reconstruction Algorithm Based on Efficient Sum of Outer Products Dictionary Learning [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 555-565. |
[7] | Fan Xinggang, Liu Jiaxian, Li Chao, Yang Youdong, Gu Wenting, Jiang Xinyang. Computer Aided Diagnosis for COVID-19 in CT Images Utilizing Transfer Learning and Attention Mechanism [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 566-581. |
[8] | Duolin, Xu Boyu, Ren Yong, Yang Xin. Magnetic Resonance Imaging Reconstruction Based on Butterfly Dilated Geometric Distillation [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 591-599. |
[9] | Li Qingwei, Fu Can, Xue Wenli, Wei Yongqiang, Shen Zhiwen. Novel State of Health Estimation for Lithium-Ion Battery Based on Differential Evolution Algorithm-Extreme Learning Machine [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(2): 252-261. |
[10] | Wang Baomin, Ding Hewei, Teng Fei, Liu Hongqin. Damage Detection of X-ray Image of Conveyor Belts with Steel Rope Cores Based on Improved FCOS Algorithm [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(2): 309-318. |
[11] | Wang Gang, Guan Yaonan, Li Dewei. Two-Stream Auto-Encoder Network for Unsupervised Skeleton-Based Action Recognition [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(2): 330-336. |
[12] | Diao Zijian, Cao Shuai, Li Wenwei, Liang Jianan, Wen Guilin, Huang Weixi, Zhang Shouming. Person Re-Identification Based on Spatial Feature Learning and Multi-Granularity Feature Fusion [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(2): 363-374. |
[13] | DING Lihui1, 2(丁黎辉), FU Lijun1, 3 (付立军), YANG Guang4(杨光), WAN Lin4, 5 (万林), CHANG Zhijun7(常志军). Video-Based Detection of Epileptic Spasms in IESS: Modeling, Detection, and Evaluation [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 1-9. |
[14] | KONG Huiyang1 (孔会扬), WANG Shuyi1 (王殊轶), ZHANG Can2 (张璨), CHEN Zan2, 3 (陈赞). Augmented Reality Navigation Using Surgical Guides Versus Conventional Techniques in Pedicle Screw Placement [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 10-17. |
[15] | ZHAO Yanfei1,2,3(赵艳飞), XIAO Peng4 (肖鹏), WANG Jingchuan1,2,3* (王景川), GUO Rui4*(郭锐). Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 27-33. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||