Medicine-Engineering Interdisciplinary Research

Automatic Removal of Multiple Artifacts for Single-Channel Electroencephalography

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  • (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 date: 2020-08-01

  Online 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.

Cite this article

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]. Journal of Shanghai Jiaotong University(Science), 2022 , 27(4) : 437 -451 . DOI: 10.1007/s12204-021-2374-5

References

[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.
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