(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-9 Issue-88 March-2022
Full-Text PDF
Paper Title : EEG artifacts detection and removal techniques for brain computer interface applications: a systematic review
Author Name : Rashmi C R and Shantala C P
Abstract :

Electroencephalogram (EEG) being the measure to record the electrical activity of the brain acts as a key factor to many brain computer interface (BCI) applications. These recorded EEG signals often get interfered with artifacts of different types such as eye blink, muscle movements, cardiac etc. Such artifacts are to be detected and removed for efficient analysis of EEG signals in pre-processing stage. Hence, this systematic review aims to provide an overview of all the available methods to remove the physiological artifacts. In addition, comparison of all the methods and their performance evaluation metrics are discussed. Relevant 159 papers are considered from the databases such as Scopus, Pubmed, Crossref, Web of Science and Google Scholar. Several analyses were made based on the collected information and current challenges for BCI applications in handling artifacts are provided. This paper also provides the details of available open-source tools for pre-processing EEG data and publicly available artifacts databases. Findings show that: a) independent component analysis (ICA) is the most popular single artifact removal method b) ICA-wavelet is the most popular hybrid artifact removal method c) maximum publications are for removal of ocular artifacts and less on muscle artifact removal d) deep learning methods are to be experimented more to improve the performance. Even though there are many methods to remove the artifacts, there is no specific method to remove all the artifacts completely. This review also shows that there are still many open issues and research opportunities to handle EEG artifacts.

Keywords : Artifacts removal, EEG, BCI, ICA.
Cite this article : Rashmi CR, Shantala CP. EEG artifacts detection and removal techniques for brain computer interface applications: a systematic review. International Journal of Advanced Technology and Engineering Exploration. 2022; 9(88):354-383. DOI:10.19101/IJATEE.2021.874883.
References :
[1]Mannan MM, Kamran MA, Jeong MY. Identification and removal of physiological artifacts from electroencephalogram signals: a review. IEEE Access. 2018; 6:30630-52.
[Crossref] [Google Scholar]
[2]Islam MK, Rastegarnia A, Yang Z. Methods for artifact detection and removal from scalp EEG: a review. Neurophysiologie Clinique/Clinical Neurophysiology. 2016; 46(4-5):287-305.
[Crossref] [Google Scholar]
[3]Jiang X, Bian GB, Tian Z. Removal of artifacts from EEG signals: a review. Sensors. 2019; 19(5):1-18.
[Crossref] [Google Scholar]
[4]Mumtaz W, Rasheed S, Irfan A. Review of challenges associated with the EEG artifact removal methods. Biomedical Signal Processing and Control. 2021.
[Crossref] [Google Scholar]
[5]https://www.bitbrain.com/blog/eeg-artifacts. Accessed 10 January 2022.
[6]Tandle A, Jog N, Dcunha P, Chheta M. Classification of artefacts in EEG signal recordings and EOG artefact removal using EOG subtraction. Commun Appl Electron. 2016; 4:12-9.
[Google Scholar]
[7]Hu J, Wang CS, Wu M, Du YX, He Y, She J. Removal of EOG and EMG artifacts from EEG using combination of functional link neural network and adaptive neural fuzzy inference system. Neurocomputing. 2015; 151:278-87.
[Crossref] [Google Scholar]
[8]Mannan MM, Jeong MY, Kamran MA. Hybrid ICA—regression: automatic identification and removal of ocular artifacts from electroencephalographic signals. Frontiers in Human Neuroscience. 2016; 10:1-17.
[Crossref] [Google Scholar]
[9]Krishnaveni V, Jayaraman S, Anitha L, Ramadoss K. Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coefficients. Journal of Neural Engineering. 2006; 3(4).
[Crossref] [Google Scholar]
[10]Safieddine D, Kachenoura A, Albera L, Birot G, Karfoul A, Pasnicu A, et al. Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches. EURASIP Journal on Advances in Signal Processing. 2012:1-15.
[Crossref] [Google Scholar]
[11]Abdullah AK, Zhang CZ, Abdullah AA, Lian S. Automatic extraction system for common artifacts in EEG signals based on evolutionary stone’s BSS algorithm. Mathematical Problems in Engineering. 2014.
[Crossref] [Google Scholar]
[12]Kroupi E, Yazdani A, Vesin JM, Ebrahimi T. Ocular artifact removal from EEG: a comparison of subspace projection and adaptive filtering methods. In European signal processing conference 2011 (pp. 1395-9). IEEE.
[Google Scholar]
[13]Li Y, Ma Z, Lu W, Li Y. Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach. Physiological Measurement. 2006; 27(4).
[Crossref] [Google Scholar]
[14]Molla MK, Islam MR, Tanaka T, Rutkowski TM. Artifact suppression from EEG signals using data adaptive time domain filtering. Neurocomputing. 2012; 97:297-308.
[Crossref] [Google Scholar]
[15]Grubov VV, Runnova AE, Koronovskii AA, Hramov AE. Adaptive filtering of electroencephalogram signals using the empirical-modes method. Technical Physics Letters. 2017; 43(7):619-22.
[Google Scholar]
[16]Quazi MH, Kahalekar SG. Artifacts removal from EEG signal: FLM optimization-based learning algorithm for neural network-enhanced adaptive filtering. Biocybernetics and Biomedical Engineering. 2017; 37(3):401-11.
[Crossref] [Google Scholar]
[17]Taulu S, Kajola M, Simola J. Suppression of interference and artifacts by the signal space separation method. Brain Topography. 2004; 16(4):269-75.
[Crossref] [Google Scholar]
[18]Nolte G, Hämäläinen MS. Partial signal space projection for artefact removal in MEG measurements: a theoretical analysis. Physics in Medicine & Biology. 2001; 46(11).
[Crossref] [Google Scholar]
[19]Taulu S, Kajola M. Presentation of electromagnetic multichannel data: the signal space separation method. Journal of Applied Physics. 2005; 97(12).
[Crossref] [Google Scholar]
[20]Song T, Gaa K, Cui L, Feffer L, Lee RR, Huang M. Evaluation of signal space separation via simulation. Medical & Biological Engineering & Computing. 2008; 46(9):923-32.
[Crossref] [Google Scholar]
[21]Song T, Cui L, Gaa K, Feffer L, Taulu S, Lee RR, Huang M. Signal space separation algorithm and its application on suppressing artifacts caused by vagus nerve stimulation for magneto encephalography recordings. Journal of Clinical Neurophysiology. 2009; 26(6):392-400.
[Crossref] [Google Scholar]
[22]Taulu S, Hari R. Removal of magnetoencephalographic artifacts with temporal signal‐space separation: demonstration with single‐trial auditory‐evoked responses. Human Brain Mapping. 2009; 30(5):1524-34.
[Crossref] [Google Scholar]
[23]Mäki H, Ilmoniemi RJ. Projecting out muscle artifacts from TMS-evoked EEG. Neuroimage. 2011; 54(4):2706-10.
[Crossref] [Google Scholar]
[24]Vosskuhl J, Mutanen TP, Neuling T, Ilmoniemi RJ, Herrmann CS. Signal-space projection suppresses the tACS artifact in EEG recordings. Frontiers in Human Neuroscience. 2020;14:1-16.
[Crossref] [Google Scholar]
[25]Klados MA, Papadelis C, Braun C, Bamidis PD. REG-ICA: a hybrid methodology combining blind source separation and regression techniques for the rejection of ocular artifacts. Biomedical Signal Processing and Control. 2011; 6(3):291-300.
[Crossref] [Google Scholar]
[26]Roy V, Shukla S. Designing efficient blind source separation methods for EEG motion artifact removal based on statistical evaluation. Wireless Personal Communications. 2019; 108(3):1311-27.
[Google Scholar]
[27]Chen X, He C, Peng H. Removal of muscle artifacts from single-channel EEG based on ensemble empirical mode decomposition and multiset canonical correlation analysis. Journal of Applied Mathematics. 2014.
[Crossref] [Google Scholar]
[28]Chen X, Chen Q, Zhang Y, Wang ZJ. A novel EEMD-CCA approach to removing muscle artifacts for pervasive EEG. IEEE Sensors Journal. 2018; 19(19):8420-31.
[Crossref] [Google Scholar]
[29]Shoker L, Sanei S, Chambers J. Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm. IEEE Signal Processing Letters. 2005; 12(10):721-4.
[Crossref] [Google Scholar]
[30]Navarro X, Porée F, Beuchée A, Carrault G. Denoising preterm EEG by signal decomposition and adaptive filtering: a comparative study. Medical Engineering & Physics. 2015; 37(3):315-20.
[Crossref] [Google Scholar]
[31]Peng H, Hu B, Shi Q, Ratcliffe M, Zhao Q, Qi Y, et al. Removal of ocular artifacts in EEG—an improved approach combining DWT and ANC for portable applications. IEEE Journal of Biomedical and Health Informatics. 2013; 17(3):600-7.
[Crossref] [Google Scholar]
[32]Nguyen HA, Musson J, Li F, Wang W, Zhang G, Xu R, et al. EOG artifact removal using a wavelet neural network. Neurocomputing. 2012; 97:374-89.
[Crossref] [Google Scholar]
[33]Erfanian A, Mahmoudi B. Real-time ocular artifact suppression using recurrent neural network for electro-encephalogram based brain-computer interface. Medical and Biological Engineering and Computing. 2005; 43(2):296-305.
[Crossref] [Google Scholar]
[34]Croft RJ, Barry RJ. EOG correction of blinks with saccade coefficients: a test and revision of the aligned-artefact average solution. Clinical Neurophysiology. 2000; 111(3):444-51.
[Crossref] [Google Scholar]
[35]He P, Wilson G, Russell C. Removal of ocular artifacts from electro-encephalogram by adaptive filtering. Medical and Biological Engineering and Computing. 2004; 42(3):407-12.
[Google Scholar]
[36]Puthusserypady S, Ratnarajah T. H/sup/spl infin//adaptive filters for eye blink artifact minimization from electroencephalogram. IEEE Signal Processing Letters. 2005; 12(12):816-9.
[Crossref] [Google Scholar]
[37]Kher R, Gandhi R. Adaptive filtering based artifact removal from electroencephalogram (EEG) signals. In international conference on communication and signal processing 2016 (pp. 0561-4). IEEE.
[Crossref] [Google Scholar]
[38]Correa AG, Laciar E, Patiño HD, Valentinuzzi ME. Artifact removal from EEG signals using adaptive filters in cascade. In journal of physics: conference series 2007 (pp. 1-10). IOP Publishing.
[Crossref] [Google Scholar]
[39]Kierkels JJ, Riani J, Bergmans JW, Van Boxtel GJ. Using an eye tracker for accurate eye movement artifact correction. IEEE Transactions on Biomedical Engineering. 2007; 54(7):1256-67.
[Crossref] [Google Scholar]
[40]Morbidi F, Garulli A, Prattichizzo D, Rizzo C, Rossi S. Application of Kalman filter to remove TMS-induced artifacts from EEG recordings. IEEE Transactions on Control Systems Technology. 2008; 16(6):1360-6.
[Crossref] [Google Scholar]
[41]Tong S, Bezerianos A, Paul J, Zhu Y, Thakor N. Removal of ECG interference from the EEG recordings in small animals using independent component analysis. Journal of Neuroscience Methods. 2001; 108(1):11-7.
[Crossref] [Google Scholar]
[42]James CJ, Gibson OJ. Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis. IEEE Transactions on Biomedical Engineering. 2003; 50(9):1108-16.
[Crossref] [Google Scholar]
[43]Joyce CA, Gorodnitsky IF, Kutas M. Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. Psychophysiology. 2004; 41(2):313-25.
[Crossref] [Google Scholar]
[44]Tran Y, Craig A, Boord P, Craig D. Using independent component analysis to remove artifact from electroencephalographic measured during stuttered speech. Medical and Biological Engineering and Computing. 2004; 42(5):627-33.
[Crossref] [Google Scholar]
[45]Zhou W, Zhou J, Zhao H, Ju L. Removing eye movement and power line artifacts from the EEG based on ICA. In engineering in medicine and biology 2006 (pp. 6017-20). IEEE.
[Crossref] [Google Scholar]
[46]Flexer A, Bauer H, Pripfl J, Dorffner G. Using ICA for removal of ocular artifacts in EEG recorded from blind subjects. Neural Networks. 2005; 18(7):998-1005.
[Crossref] [Google Scholar]
[47]Mognon A, Jovicich J, Bruzzone L, Buiatti M. ADJUST: an automatic EEG artifact detector based on the joint use of spatial and temporal features. Psychophysiology. 2011; 48(2):229-40.
[Crossref] [Google Scholar]
[48]Nakamura W, Anami K, Mori T, Saitoh O, Cichocki A, Amari SI. Removal of ballistocardiogram artifacts from simultaneously recorded EEG and fMRI data using independent component analysis. IEEE Transactions on Biomedical Engineering. 2006; 53(7):1294-308.
[Crossref] [Google Scholar]
[49]Miljković N, Matić V, Van HS, Popović MB. Independent component analysis (ICA) methods for neonatal EEG artifact extraction: sensitivity to variation of artifact properties. In symposium on neural network applications in electrical engineering 2010 (pp. 19-21). IEEE.
[Crossref] [Google Scholar]
[50]Wang Y, Jung TP. Improving brain–computer interfaces using independent component analysis. In towards practical brain-computer interfaces 2012 (pp. 67-83). Springer, Berlin, Heidelberg.
[Crossref] [Google Scholar]
[51]Turnip A. JADE-ICA algorithm for EOG artifact removal in EEG recording. In international conference on technology, informatics, management, engineering & environment 2014 (pp. 270-4). IEEE.
[Crossref] [Google Scholar]
[52]Zou Y, Nathan V, Jafari R. Automatic identification of artifact-related independent components for artifact removal in EEG recordings. IEEE Journal of Biomedical and Health Informatics. 2014; 20(1):73-81.
[Crossref] [Google Scholar]
[53]Lakshmi KA, Surling SN, Sheeba O. A novel approach for the removal of artifacts in EEG signals. In international conference on wireless communications, signal processing and networking 2017 (pp. 2595-9). IEEE.
[Crossref] [Google Scholar]
[54]Daly I, Billinger M, Scherer R, Müller-putz G. On the automated removal of artifacts related to head movement from the EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2013; 21(3):427-34.
[Crossref] [Google Scholar]
[55]Mayeli A, Zotev V, Refai H, Bodurka J. Real-time EEG artifact correction during fMRI using ICA. Journal of Neuroscience Methods. 2016; 274:27-37.
[Crossref] [Google Scholar]
[56]De CW, Vergult A, Vanrumste B, Van PW, Van HS. Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. IEEE Transactions on Biomedical Engineering. 2006; 53(12):2583-7.
[Crossref] [Google Scholar]
[57]Chou CC, Chen TY, Fang WC. FPGA implementation of EEG system-on-chip with automatic artifacts removal based on BSS-CCA method. In biomedical circuits and systems conference 2016 (pp. 224-7). IEEE.
[Crossref] [Google Scholar]
[58]Turnip A. Automatic artifacts removal of EEG signals using robust principal component analysis. In 2nd international conference on technology, informatics, management, engineering & environment 2014 (pp. 331-4). IEEE.
[Crossref] [Google Scholar]
[59]Ter BEM, De JB, Van PMJ. Reduction of TMS induced artifacts in EEG using principal component analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2013; 21(3):376-82.
[Crossref] [Google Scholar]
[60]Kiamini M, Alirezaee S, Perseh B, Ahmadi M. A wavelet based algorithm for ocular artifact detection in the EEG signals. In international multitopic conference 2008 (pp. 165-8). IEEE.
[Crossref] [Google Scholar]
[61]Islam MK, Rastegarnia A, Yang Z. A wavelet-based artifact reduction from scalp EEG for epileptic seizure detection. IEEE Journal of Biomedical and Health Informatics. 2015; 20(5):1321-32.
[Crossref] [Google Scholar]
[62]Islam MK, Rastegarnia A. Probability mapping based artifact detection and wavelet denoising based artifact removal from scalp EEG for BCI applications. In international conference on computer and communication systems 2019 (pp. 243-7). IEEE.
[Crossref] [Google Scholar]
[63]Bajaj N, Carrión JR, Bellotti F, Berta R, De GA. Automatic and tunable algorithm for EEG artifact removal using wavelet decomposition with applications in predictive modeling during auditory tasks. Biomedical Signal Processing and Control. 2020.
[Crossref] [Google Scholar]
[64]Liu Y, An F, Lang X, Dai Y. Remove motion artifacts from scalp single channel EEG based on noise assisted least square multivariate empirical mode decomposition. In international congress on image and signal processing, biomedical engineering and informatics 2020 (pp. 568-73). IEEE.
[Crossref] [Google Scholar]
[65]Tavildar S, Ashrafi A. Application of multivariate empirical mode decomposition and canonical correlation analysis for EEG motion artifact removal. In conference on advances in signal processing 2016 (pp. 150-4). IEEE.
[Crossref] [Google Scholar]
[66]Vijayasankar A, Kumar PR. Correction of blink artifacts from single channel EEG by EMD-IMF thresholding. In conference on signal processing and communication engineering systems 2018 (pp. 176-80). IEEE.
[Crossref] [Google Scholar]
[67]Liu Y, Zhou Y, Lang X, Liu Y, Zheng Q, Zhang Y, et al. An efficient and robust muscle artifact removal method for few-channel EEG. IEEE Access. 2019; 7:176036-50.
[Crossref] [Google Scholar]
[68]Shao SY, Shen KQ, Ong CJ, Wilder-Smith EP. Automatic EEG artifact removal: a weighted support vector machine approach with error correction. IEEE Transactions on Biomedical Engineering. 2008; 56(2):336-44.
[Crossref] [Google Scholar]
[69]Paulraj MP, Yaccob SB, Yogesh CK. Fractal feature based detection of muscular and ocular artifacts in EEG signals. In conference on biomedical engineering and sciences 2014 (pp. 916-21). IEEE.
[Crossref] [Google Scholar]
[70]Tibdewal MN, Thakare KB. ANN based automatic identification and classification of ocular artifacts and non-artifactual EEG. In second international conference on intelligent computing and control systems 2018 (pp. 980-5). IEEE.
[Crossref] [Google Scholar]
[71]Saifutdinova E, Dudysová DU, Lhotská L, Gerla V, Macaš M. Artifact detection in multichannel sleep EEG using random forest classifier. In international conference on bioinformatics and biomedicine 2018 (pp. 2803-5). IEEE.
[Crossref] [Google Scholar]
[72]Park HJ, Jeong DU, Park KS. Automated detection and elimination of periodic ECG artifacts in EEG using the energy interval histogram method. IEEE transactions on Biomedical Engineering. 2002; 49(12):1526-33.
[Crossref] [Google Scholar]
[73]De Clercq W, Vanrumste B, Papy JM, Van Paesschen W, Van Huffel S. Modeling common dynamics in multichannel signals with applications to artifact and background removal in EEG recordings. IEEE Transactions on Biomedical Engineering. 2005; 52(12):2006-15.
[Crossref] [Google Scholar]
[74]Gao JF, Yang Y, Lin P, Wang P, Zheng CX. Automatic removal of eye-movement and blink artifacts from EEG signals. Brain Topography. 2010; 23(1):105-14.
[Google Scholar]
[75]Chen CK, Chua E, Hsieh ZH, Fang WC, Wang YT, Jung TP. An EEG-based brain—computer interface with real-time artifact removal using independent component analysis. In second international conference on consumer electronics-berlin 2012 (pp. 13-4). IEEE.
[Crossref] [Google Scholar]
[76]Acharjee PP, Phlypo R, Wu L, Calhoun VD, Adalı T. Independent vector analysis for gradient artifact removal in concurrent EEG-fMRI data. IEEE Transactions on Biomedical Engineering. 2015; 62(7):1750-8.
[Crossref] [Google Scholar]
[77]Maddirala AK, Shaik RA. Motion artifact removal from single channel electroencephalogram signals using singular spectrum analysis. Biomedical Signal Processing and Control. 2016; 30:79-85.
[Crossref] [Google Scholar]
[78]Li X, Guan C, Zhang H, Ang KK. Discriminative ocular artifact correction for feature learning in EEG analysis. IEEE Transactions on Biomedical Engineering. 2016; 64(8):1906-13.
[Crossref] [Google Scholar]
[79]Mohammadpour M, Rahmani V. A hidden markov model-based approach to removing EEG artifact. In Iranian joint congress on fuzzy and intelligent systems 2017 (pp. 46-9). IEEE.
[Crossref] [Google Scholar]
[80]Chen X, Liu A, Chen Q, Liu Y, Zou L, Mckeown MJ. Simultaneous ocular and muscle artifact removal from EEG data by exploiting diverse statistics. Computers in Biology and Medicine. 2017; 88:1-10.
[Crossref] [Google Scholar]
[81]Borowicz A. Using a multichannel wiener filter to remove eye-blink artifacts from EEG data. Biomedical Signal Processing and Control. 2018; 45:246-55.
[Crossref] [Google Scholar]
[82]Ahmad I, Shufian A, Barno MA, Datta S. A novel approach to remove ocular artifact from EEG signal. In international conference for convergence in technology 2019 (pp. 1-4). IEEE.
[Crossref] [Google Scholar]
[83]Dai C, Wang J, Xie J, Li W, Gong Y, Li Y. Removal of ECG artifacts from EEG using an effective recursive least square notch filter. IEEE Access. 2019; 7:158872-80.
[Crossref] [Google Scholar]
[84]Butkevičiūtė E, Bikulčienė L, Sidekerskienė T, Blažauskas T, Maskeliūnas R, Damaševičius R, et al. Removal of movement artefact for mobile EEG analysis in sports exercises. IEEE Access. 2019; 7:7206-17.
[Crossref] [Google Scholar]
[85]Dimigen O. Optimizing the ICA-based removal of ocular EEG artifacts from free viewing experiments. NeuroImage. 2020.
[Crossref] [Google Scholar]
[86]Li Y, Liu J, Tang C, Han W, Zhou S, Yang S, et al. Multiscale entropy analysis of instantaneous frequency variation to overcome the cross-over artifact in rhythmic EEG. IEEE Access. 2021; 9:12896-905.
[Crossref] [Google Scholar]
[87]Sawangjai P, Trakulruangroj M, Boonnag C, Piriyajitakonkij M, Tripathy RK, Sudhawiyangkul T, et al. EEGANet: removal of ocular artifact from the EEG signal using generative adversarial networks. IEEE Journal of Biomedical and Health Informatics. 2021.
[Crossref] [Google Scholar]
[88]Castellanos NP, Makarov VA. Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis. Journal of Neuroscience Methods. 2006; 158(2):300-12.
[Crossref] [Google Scholar]
[89]Mammone N, La Foresta F, Morabito FC. Automatic artifact rejection from multichannel scalp EEG by wavelet ICA. IEEE Sensors Journal. 2011; 12(3):533-42.
[Crossref] [Google Scholar]
[90]Zachariah A, Jai J, Titus G. Automatic EEG artifact removal by independent component analysis using critical EEG rhythms. In international conference on control communication and computing 2013 (pp. 364-7). IEEE.
[Crossref] [Google Scholar]
[91]Kaur C, Singh P. EEG artifact suppression based on SOBI based ICA using wavelet thresholding. In international conference on recent advances in engineering & computational sciences 2015 (pp. 1-4). IEEE.
[Crossref] [Google Scholar]
[92]Akhtar MT, James CJ. Focal artifact removal from ongoing EEG–a hybrid approach based on spatially-constrained ICA and wavelet de-noising. In annual international conference of the IEEE engineering in medicine and biology society 2009 (pp. 4027-30). IEEE.
[Crossref] [Google Scholar]
[93]Ghandeharion H, Erfanian A. A fully automatic ocular artifact suppression from EEG data using higher order statistics: improved performance by wavelet analysis. Medical Engineering & Physics. 2010; 32(7):720-9.
[Crossref] [Google Scholar]
[94]Jirayucharoensak S, Israsena P. Automatic removal of EEG artifacts using ICA and lifting wavelet transform. In international computer science and engineering conference 2013 (pp. 136-9). IEEE.
[Crossref] [Google Scholar]
[95]Mahajan R, Morshed BI. Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy, kurtosis, and wavelet-ICA. IEEE journal of Biomedical and Health Informatics. 2014; 19(1):158-65.
[Crossref] [Google Scholar]
[96]Paradeshi KP, Scholar R, Kolekar UD. Removal of ocular artifacts from multichannel EEG signal using wavelet enhanced ICA. In international conference on energy, communication, data analytics and soft computing 2017 (pp. 383-7). IEEE.
[Crossref] [Google Scholar]
[97]Hsu WY, Lin CH, Hsu HJ, Chen PH, Chen IR. Wavelet-based envelope features with automatic EOG artifact removal: application to single-trial EEG data. Expert Systems with Applications. 2012; 39(3):2743-9.
[Crossref] [Google Scholar]
[98]Cheng J, Li L, Li C, Liu Y, Liu A, Qian R, et al. Remove diverse artifacts simultaneously from a single-channel EEG based on SSA and ICA: a semi-simulated study. IEEE Access. 2019; 7:60276-89.
[Crossref] [Google Scholar]
[99]Devulapalli SP, Rao S, Satya PK. FLM-based optimization scheme for ocular artifacts removal in EEG signals. In microelectronics, electromagnetics and telecommunications 2021 (pp. 777-82). Springer, Singapore.
[Crossref] [Google Scholar]
[100]Abidi A, Nouira I, Assali I, Saafi MA, Bedoui MH. Hybrid multi-channel eeg filtering method for ocular and muscular artifact removal based on the 3D spline interpolation technique. The Computer Journal. 2021.
[Crossref] [Google Scholar]
[101]Chen Q, Li Y, Yuan X. A hybrid method for muscle artifact removal from EEG signals. Journal of Neuroscience Methods. 2021.
[Crossref] [Google Scholar]
[102]Jafarifarmand A, Badamchizadeh MA. Artifacts removal in EEG signal using a new neural network enhanced adaptive filter. Neurocomputing. 2013; 103:222-31.
[Crossref] [Google Scholar]
[103]Wang Z, Xu P, Liu T, Tian Y, Lei X, Yao D. Robust removal of ocular artifacts by combining independent component analysis and system identification. Biomedical Signal Processing and Control. 2014; 10:250-9.
[Crossref] [Google Scholar]
[104]Dora C, Patro RN, Rout SK, Biswal PK, Biswal B. Adaptive SSA based muscle artifact removal from single channel EEG using neural network regressor. IRBM. 2021; 42(5):324-33.
[Crossref] [Google Scholar]
[105]Schetinin V, Schult J. The combined technique for detection of artifacts in clinical electroencephalograms of sleeping newborns. IEEE Transactions on Information Technology in Biomedicine. 2004; 8(1):28-35.
[Crossref] [Google Scholar]
[106]Halder S, Bensch M, Mellinger J, Bogdan M, Kübler A, Birbaumer N, et al. Online artifact removal for brain-computer interfaces using support vector machines and blind source separation. Computational Intelligence and Neuroscience. 2007.
[Crossref] [Google Scholar]
[107]Nazarpour K, Wongsawat Y, Sanei S, Chambers JA, Oraintara S. Removal of the eye-blink artifacts from EEGs via STF-TS modeling and robust minimum variance beamforming. IEEE Transactions on Biomedical Engineering. 2008; 55(9):2221-31.
[Crossref] [Google Scholar]
[108]Chan HL, Tsai YT, Meng LF, Wu T. The removal of ocular artifacts from EEG signals using adaptive filters based on ocular source components. Annals of Biomedical Engineering. 2010; 38(11):3489-99.
[Crossref] [Google Scholar]
[109]Vázquez RR, Velez-Perez H, Ranta R, Dorr VL, Maquin D, Maillard L. Blind source separation, wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling. Biomedical Signal Processing and Control. 2012; 7(4):389-400.
[Crossref] [Google Scholar]
[110]Matsusaki F, Ikuno T, Katayama Y, Iramina K. Online artifact removal in EEG signals. In world congress on medical physics and biomedical engineering, Beijing, China 2013 (pp. 352-5). Springer, Berlin, Heidelberg.
[Crossref] [Google Scholar]
[111]Roy RN, Charbonnier S, Bonnet S. Eye blink characterization from frontal EEG electrodes using source separation and pattern recognition algorithms. Biomedical Signal Processing and Control. 2014; 14:256-64.
[Crossref] [Google Scholar]
[112]Hamaneh MB, Chitravas N, Kaiboriboon K, Lhatoo SD, Loparo KA. Automated removal of EKG artifact from EEG data using independent component analysis and continuous wavelet transformation. IEEE Transactions on Biomedical Engineering. 2013; 61(6):1634-41.
[Crossref] [Google Scholar]
[113]Zhao Q, Hu B, Shi Y, Li Y, Moore P, Sun M, Peng H. Automatic identification and removal of ocular artifacts in EEG—improved adaptive predictor filtering for portable applications. IEEE Transactions on Nanobioscience. 2014; 13(2):109-17.
[Crossref] [Google Scholar]
[114]Daly I, Scherer R, Billinger M, Müller-putz G. FORCe: fully online and automated artifact removal for brain-computer interfacing. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2014; 23(5):725-36.
[Crossref] [Google Scholar]
[115]Winkler I, Debener S, Müller KR, Tangermann M. On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP. In annual international conference of the IEEE engineering in medicine and biology society 2015 (pp. 4101-5). IEEE.
[Crossref] [Google Scholar]
[116]Bono V, Das S, Jamal W, Maharatna K. Hybrid wavelet and EMD/ICA approach for artifact suppression in pervasive EEG. Journal of Neuroscience Methods. 2016; 267:89-107.
[Crossref] [Google Scholar]
[117]Kim CS, Sun J, Liu D, Wang Q, Paek SG. Removal of ocular artifacts using ICA and adaptive filter for motor imagery-based BCI. IEEE/CAA Journal of Automatica Sinica. 2017.
[Crossref] [Google Scholar]
[118]Radüntz T, Scouten J, Hochmuth O, Meffert B. Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features. Journal of Neural Engineering. 2017; 14(4):1-8.
[Crossref] [Google Scholar]
[119]Chavez M, Grosselin F, Bussalb A, Fallani FD, Navarro-sune X. Surrogate-based artifact removal from single-channel EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2018; 26(3):540-50.
[Crossref] [Google Scholar]
[120]Barua S, Ahmed MU, Ahlstrom C, Begum S, Funk P. Automated EEG artifact handling with application in driver monitoring. IEEE Journal of Biomedical and Health Informatics. 2017; 22(5):1350-61.
[Crossref] [Google Scholar]
[121]Song Y, Sepulveda F. A novel technique for selecting EMG-contaminated EEG channels in self-paced brain–computer interface task onset. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2018; 26(7):1353-62.
[Crossref] [Google Scholar]
[122]Janani AS, Grummett TS, Lewis TW, Fitzgibbon SP, Whitham EM, Delosangeles D, et al. Improved artefact removal from EEG using canonical correlation analysis and spectral slope. Journal of Neuroscience Methods. 2018; 298:1-15.
[Crossref] [Google Scholar]
[123]Richer N, Downey RJ, Hairston WD, Ferris DP, Nordin AD. Motion and muscle artifact removal validation using an electrical head phantom, robotic motion platform, and dual layer mobile EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2020; 28(8):1825-35.
[Crossref] [Google Scholar]
[124]Ahmed MA, Qi D, Alshemmary EN. Effective hybrid method for the detection and rejection of electrooculogram (EOG) and power line noise artefacts from electroencephalogram (EEG) mixtures. IEEE Access. 2020; 8:202919-32.
[Crossref] [Google Scholar]
[125]Sheela P, Puthankattil SD. A hybrid method for artifact removal of visual evoked EEG. Journal of Neuroscience Methods. 2020.
[Crossref] [Google Scholar]
[126]Noorbasha SK, Sudha GF. Removal of EOG artifacts and separation of different cerebral activity components from single channel EEG—an efficient approach combining SSA–ICA with wavelet thresholding for BCI applications. Biomedical Signal Processing and Control. 2021.
[Crossref] [Google Scholar]
[127]Shahbakhti M, Beiramvand M, Nazari M, Broniec-wójcik A, Augustyniak P, Rodrigues AS, et al. VME-DWT: an efficient algorithm for detection and elimination of eye blink from short segments of single EEG channel. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2021; 29:408-17.
[Crossref] [Google Scholar]
[128]Jamil Z, Jamil A, Majid M. Artifact removal from EEG signals recorded in non-restricted environment. Biocybernetics and Biomedical Engineering. 2021; 41(2):503-15.
[Crossref] [Google Scholar]
[129]Trigui O, Daoud S, Ghorbel M, Dammak M, Mhiri C, Ben HA. Removal of eye blink artifacts from EEG signal using morphological modeling and orthogonal projection. Signal, Image and Video Processing. 2022; 16(1):19-27.
[Crossref] [Google Scholar]
[130]Chiu NT, Huwiler S, Ferster ML, Karlen W, Wu HT, Lustenberger C. Get rid of the beat in mobile EEG applications: A framework towards automated cardiogenic artifact detection and removal in single-channel EEG. Biomedical Signal Processing and Control. 2022; 72:103220.
[Crossref] [Google Scholar]
[131]Kierkels JJ, Van BGJ, Vogten LL. A model-based objective evaluation of eye movement correction in EEG recordings. IEEE Transactions on Biomedical Engineering. 2006; 53(2):246-53.
[Crossref] [Google Scholar]
[132]Fitzgibbon SP, Powers DM, Pope KJ, Clark CR. Removal of EEG noise and artifact using blind source separation. Journal of Clinical Neurophysiology. 2007; 24(3):232-43.
[Crossref] [Google Scholar]
[133]Urigüen JA, Garcia-zapirain B. EEG artifact removal—state-of-the-art and guidelines. Journal of Neural Engineering. 2015; 12(3).
[Crossref] [Google Scholar]
[134]Sweeney KT, Ward TE, Mcloone SF. Artifact removal in physiological signals-practices and possibilities. IEEE Transactions on Information Technology in Biomedicine. 2012; 16(3):488-500.
[Crossref] [Google Scholar]
[135]Sai CY, Mokhtar N, Arof H, Cumming P, Iwahashi M. Automated classification and removal of EEG artifacts with SVM and wavelet-ICA. IEEE Journal of Biomedical and Health Informatics. 2017; 22(3):664-70.
[Crossref] [Google Scholar]
[136]Ghosh R, Sinha N, Biswas SK. Automated eye blink artefact removal from EEG using support vector machine and autoencoder. IET Signal Processing. 2019; 13(2):141-8.
[Crossref] [Google Scholar]
[137]Bartels G, Shi LC, Lu BL. Automatic artifact removal from EEG-a mixed approach based on double blind source separation and support vector machine. In annual international conference of the IEEE engineering in medicine and biology 2010 (pp. 5383-6). IEEE.
[Crossref] [Google Scholar]
[138]Phadikar S, Sinha N, Ghosh R. Automatic EEG eyeblink artefact identification and removal technique using independent component analysis in combination with support vector machines and denoising autoencoder. IET Signal Processing. 2020; 14(6):396-405.
[Crossref] [Google Scholar]
[139]Dora C, Biswal PK. An ELM based regression model for ECG artifact minimization from single channel EEG. In international conference on intelligent data engineering and automated learning 2018 (pp. 269-76). Springer, Cham.
[Crossref] [Google Scholar]
[140]Nedelcu E, Portase R, Tolas R, Muresan R, Dinsoreanu M, Potolea R. Artifact detection in EEG using machine learning. In international conference on intelligent computer communication and processing 2017 (pp. 77-83). IEEE.
[Crossref] [Google Scholar]
[141]Dora C, Biswal PK. Robust ECG artifact removal from EEG using continuous wavelet transformation and linear regression. In international conference on signal processing and communications 2016 (pp. 1-5). IEEE.
[Crossref] [Google Scholar]
[142]Behera S, Mohanty MN. Detection of ocular artifacts using bagged tree ensemble model. In international conference on applied machine learning 2019 (pp. 44-7). IEEE.
[Crossref] [Google Scholar]
[143]Le TH. A deep wavelet sparse autoencoder method for online and automatic electrooculographical artifact removal. Neural Computing and Applications. 2020; 32(24):18255-70.
[Crossref] [Google Scholar]
[144]Yang B, Duan K, Fan C, Hu C, Wang J. Automatic ocular artifacts removal in EEG using deep learning. Biomedical Signal Processing and Control. 2018; 43:148-58.
[Crossref] [Google Scholar]
[145]Routray L, Biswal P, Pattanaik SR. ECG artifact removal of EEG signal using adaptive neural network. In 13th international conference on industrial and information systems (ICIIS) 2018 (pp. 103-6). IEEE.
[Crossref] [Google Scholar]
[146]Lee SS, Lee K, Kang G. EEG artifact removal by bayesian deep learning & ICA. In 42nd annual international conference of the IEEE engineering in medicine & biology society 2020 (pp. 932-5). IEEE.
[Crossref] [Google Scholar]
[147]Kim DK, Keene S. Fast automatic artifact annotator for EEG signals using deep learning. In biomedical signal processing 2021 (pp. 195-221). Springer, Cham.
[Google Scholar]
[148]Sun W, Su Y, Wu X, Wu X. A novel end-to-end 1D-ResCNN model to remove artifact from EEG signals. Neurocomputing. 2020; 404:108-21.
[Crossref] [Google Scholar]
[149]Zhang H, Wei C, Zhao M, Liu Q, Wu H. A novel convolutional neural network model to remove muscle artifacts from EEG. In international conference on acoustics, speech and signal processing 2021 (pp. 1265-9). IEEE.
[Crossref] [Google Scholar]
[150]Zhang H, Zhao M, Wei C, Mantini D, Li Z, Liu Q. Eegdenoisenet: a benchmark dataset for deep learning solutions of EEG denoising. Journal of Neural Engineering. 2021; 18(5).
[Crossref] [Google Scholar]
[151]Mannan MM, Kim S, Jeong MY, Kamran MA. Hybrid EEG—eye tracker: automatic identification and removal of eye movement and blink artifacts from electroencephalographic signal. Sensors. 2016; 16(2):241.
[Crossref] [Google Scholar]
[152]Schlögl A, Keinrath C, Zimmermann D, Scherer R, Leeb R, Pfurtscheller G. A fully automated correction method of EOG artifacts in EEG recordings. Clinical Neurophysiology. 2007; 118(1):98-104.
[Crossref] [Google Scholar]
[153]Valipour S, Kulkarni GR, Shaligram AD. Study on performance metrics for consideration of efficiency of the ocular artifact removal algorithms for EEG signals. Indian Journal of Science and Technology. 2015; 8(30):1-6.
[Crossref] [Google Scholar]
[154]https://sccn.ucsd.edu/eeglab/index.php. Accessed 10 January 2022.
[155]https://www.fieldtriptoolbox.org/. Accessed 10 January 2022.
[156]https://mne.tools/stable/index.html. Accessed 10 January 2022.
[157]Kobler RJ, Sburlea AI, Mondini V, Müller-Putz GR. HEAR to remove pops and drifts: the high-variance electrode artifact removal (HEAR) algorithm. In annual international conference of the IEEE engineering in medicine and biology society 2019 (pp. 5150-5). IEEE.
[Crossref] [Google Scholar]
[158]https://www.tugraz.at/institute/ine/research/software. Accessed 10 October 2021.
[159]Nolan H, Whelan R, Reilly RB. FASTER: fully automated statistical thresholding for EEG artifact rejection. Journal of Neuroscience Methods. 2010; 192(1):152-62.
[Crossref] [Google Scholar]
[160]Nicolaou N, Nasuto SJ. Automatic artefact removal from event-related potentials via clustering. The Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology. 2007; 48(1):173-83.
[Google Scholar]
[161]Hartmann MM, Schindler K, Gebbink TA, Gritsch G, Kluge T. PureEEG: automatic EEG artifact removal for epilepsy monitoring. Neurophysiologie Clinique/Clinical Neurophysiology. 2014; 44(5):479-90.
[Crossref] [Google Scholar]
[162]https://www.emotiv.com/pureeeg/. Accessed 10 October 2021.
[163]https://sameni.org/OSET/. Accessed 10 October 2021.
[164]https://irenne.github.io/artifacts/. Accessed 10 October 2021.
[165]Cheema MS, Dutta A. Automatic independent component scalp map analysis of electroencephalogram during motor preparation. In annual international conference of the engineering in medicine and biology society 2018 (pp. 4689-92). IEEE.
[Crossref] [Google Scholar]
[166]Gómez-Herrero G. Automatic artifact removal (AAR) toolbox v1. 3 (Release 09.12. 2007) for MATLAB. Tampere University of Technology. 2007.
[Google Scholar]
[167]https://www.nitrc.org/projects/adjust/. Accessed 10 January 2022.
[168]Chen X, Liu Q, Tao W, Li L, Lee S, Liu A, et al. ReMAE: user-friendly toolbox for removing muscle artifacts from EEG. IEEE Transactions on Instrumentation and Measurement. 2019; 69(5):2105-19.
[Crossref] [Google Scholar]