Signal Artifacts and Techniques for Artifacts and Noise Removal
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Artifacts originate from the environment, the person wearing the wearable, and the device itself.
“Artifacts originate from the environment, the person wearing the wearable, and the device itself41.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [41]
Cites
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371 · see citing works
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680 · see citing works
Tensor Decompositions and Applications
Kolda, T. G., & Bader, B. W. (2009) · SIAM Review · doi:10.1137/07070111x · Cited by 10,595 · see citing works
Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/156869 · Cited by 10,323 · see citing works
Receptive fields of single neurones in the cat's striate cortex
Hubel, D. H., & Wiesel, T. N. (1959) · The Journal of Physiology · doi:10.1113/jphysiol.1959.sp006308 · Cited by 4,653 · see citing works
Brainstorm: A User-Friendly Application for MEG/EEG Analysis
Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D., & Leahy, R. M. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/879716 · Cited by 3,625 · see citing works
Removing electroencephalographic artifacts by blind source separation
Jung, T., Makeig, S., Humphries, C., Lee, T.-W., McKeown, M. J., Iragui, V. J., & B, T. J. S. A. (2000) · Psychophysiology · doi:10.1111/1469-8986.3720163 · Cited by 3,342 · see citing works
A Wavelet Tour of Signal Processing : The Sparse Way
Mallat, S., & Peyré, G. (2008) · Cited by 2,759 · see citing works
Analyzing Neural Time Series Data: Theory and Practice
Cohen, M. X. (2014) · doi:10.7551/mitpress/9609.001.0001 · Cited by 2,105 · see citing works
Jung, T., Makeig, S., Westerfield, M., Townsend, J., Courchesne, E., & B, T. J. S. A. (2000) · Clinical Neurophysiology · doi:10.1016/s1388-2457(00)00386-2 · Cited by 1,326 · see citing works
ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features
Mognon, A., Jovicich, J., Bruzzone, L., & Buiatti, M. (2010) · Psychophysiology · doi:10.1111/j.1469-8986.2010.01061.x · Cited by 1,274 · see citing works
Akaike Information Criterion Statistics.
Grove, D., Sakamoto, Y., Ishiguro, M., & Kitagawa, G. (1988) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2348776 · Cited by 1,184 · see citing works
FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection
Nolan, H., Whelan, R., & Reilly, R. B. (2010) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2010.07.015 · Cited by 1,080 · see citing works
Chaumon, M., Bishop, D., & Busch, N. A. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.02.025 · Cited by 833 · see citing works
Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals
Winkler, I., Haufe, S., & Tangermann, M. (2011) · Behavioral and Brain Functions · doi:10.1186/1744-9081-7-30 · Cited by 782 · see citing works
Akaike Information Criterion Statistics
Brooks, D. G., Sakamoto, Y., Ishiguro, M., & Kitagawa, G. (1989) · Technometrics · doi:10.2307/1268842 · Cited by 769 · see citing works
Castellanos, N. P., & Makarov, V. A. (2006) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2006.05.033 · Cited by 571 · see citing works
Removal of FMRI environment artifacts from EEG data using optimal basis sets
Niazy, R. K., Beckmann, C. F., Iannetti, G. D., Brady, J. M., & Smith, S. M. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.06.067 · Cited by 569 · see citing works
Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram
Clercq, W. D., Vergult, A., Vanrumste, B., Paesschen, W. V., & Huffel, S. V. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.879459 · Cited by 522 · see citing works
Methods for artifact detection and removal from scalp EEG: A review
Islam, M. K., Rastegarnia, A., & Yang, Z. Q. (2016) · Neurophysiologie Clinique · doi:10.1016/j.neucli.2016.07.002 · Cited by 457 · see citing works
Self-citationThe impact of epilepsy from the patient's perspective I. Descriptions and subjective perceptions
Fisher, R. S., Vickrey, B. G., Gibson, P., Hermann, B., Penovich, P., Scherer, A., & Walker, S. (2000) · Epilepsy Research · doi:10.1016/s0920-1211(00)00126-1 · Cited by 438 · see citing works
BCILAB: a platform for brain–computer interface development
Kothe, C. A., & Makeig, S. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/5/056014 · Cited by 412 · see citing works
Massively Parallel Recording of Unit and Local Field Potentials With Silicon-Based Electrodes
Csicsvari, J., Henze, D. A., Jamieson, B., Harris, K. D., Sirota, A., Barthó, P., Wise, K. D., & Buzsáki, G. (2003) · Journal of Neurophysiology · doi:10.1152/jn.00116.2003 · Cited by 402 · see citing works
Semi-automatic identification of independent components representing EEG artifact
Viola, F. C., Thorne, J. D., Edmonds, B. A., Schneider, T. R., Eichele, T., & Debener, S. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2009.01.015 · Cited by 364 · see citing works
Artifact Removal in Physiological Signals—Practices and Possibilities
Sweeney, K. T., Ward, T. E., & McLoone, S. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2012.2188536 · Cited by 358 · see citing works
The Variational Bayes Method in Signal Processing
Šmídl, V., & Quinn, A. (2006) · Springer eBooks · doi:10.1007/3-540-28820-1 · Cited by 338 · see citing works
Robust artifactual independent component classification for BCI practitioners
Winkler, I., Brandl, S., Horn, F., Waldburger, E., Allefeld, C., & Tangermann, M. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/3/035013 · Cited by 328 · see citing works
EMGLAB: An interactive EMG decomposition program
McGill, K. C., Lateva, Z. C., & Marateb, H. R. (2005) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2005.05.015 · Cited by 312 · see citing works
Sweeney, K. T., McLoone, S., & Ward, T. E. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2225427 · Cited by 299 · see citing works
James, C. J., & Gibson, O. (2003) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.816076 · Cited by 263 · see citing works
The 128-Channel Fully Differential Digital Integrated Neural Recording and Stimulation Interface
Shahrokhi, F., Abdelhalim, K., Serletis, D., Carlen, P. L., & Genov, R. (2010) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2010.2041350 · Cited by 263 · see citing works
Variational Bayesian Super Resolution
Babacan, S. D., Molina, R., & Katsaggelos, A. K. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2080278 · Cited by 260 · see citing works
Mahajan, R., & Morshed, B. I. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2333010 · Cited by 251 · see citing works
BioSig: The Free and Open Source Software Library for Biomedical Signal Processing
Vidaurre, C., Sander, T. H., & Schlögl, A. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/935364 · Cited by 220 · see citing works
An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave
Silva, I., & Moody, G. B. (2014) · Journal of Open Research Software · doi:10.5334/jors.bi · Cited by 211 · see citing works
Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach
Li, Y., Ma, Z., Lu, W., & Li, Y. (2006) · Physiological Measurement · doi:10.1088/0967-3334/27/4/008 · Cited by 196 · see citing works
Akhtar, M. T., Mitsuhashi, W., & James, C. J. (2011) · Signal Processing · doi:10.1016/j.sigpro.2011.08.005 · Cited by 189 · see citing works
Klados, M. A., Papadelis, C. L., Braun, C., & Bamidis, P. D. (2011) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2011.02.001 · Cited by 176 · see citing works
FORCe: Fully online and automated artifact removal for brain-computer interfacing
Daly, I., Scherer, R., Billinger, M., & Muller-Putz, G. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2014.2346621 · Cited by 174 · see citing works
Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm
Shoker, L., Sanei, S., & Chambers, J. A. (2005) · IEEE Signal Processing Letters · doi:10.1109/lsp.2005.855539 · Cited by 159 · see citing works
Self-citationAutomatic EEG Artifact Removal: A Weighted Support Vector Machine Approach With Error Correction
Shao, S., Shen, K., Ong, C. J., Wilder-Smith, E. P., & Li, X. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.2005969 · Cited by 151 · see citing works
Automatic ocular artifacts removal in EEG using deep learning
Yang, B., Duan, K., Fan, C., Hu, C., & Wang, J. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.02.021 · Cited by 151 · see citing works
A Methodology for Validating Artifact Removal Techniques for Physiological Signals
Sweeney, K. T., Ayaz, H., Ward, T. E., İzzetoğlu, M., McLoone, S., & Onaral, B. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2012.2207400 · Cited by 138 · see citing works
Peng, H., Hu, B., Shi, Q., Ratcliffe, M., Zhao, Q., Qi, Y., & Gao, G. (2013) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2253614 · Cited by 137 · see citing works
Lawhern, V. J., Hairston, W. D., McDowell, K., Westerfield, M., & Robbins, K. A. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.05.017 · Cited by 135 · see citing works
A Wavelet-Based Artifact Reduction From Scalp EEG for Epileptic Seizure Detection
Islam, M. K., Rastegarnia, A., & Yang, Z. Q. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2457093 · Cited by 117 · see citing works
Self-citationANSLAB: Integrated multichannel peripheral biosignal processing in psychophysiological science
Blechert, J., Peyk, P., Liedlgruber, M., & Wilhelm, F. H. (2015) · Behavior Research Methods · doi:10.3758/s13428-015-0665-1 · Cited by 113 · see citing works
EOG artifact removal using a wavelet neural network
Nguyen, H.-A. T., Musson, J., Li, F., Wang, W., Zhang, G., Xu, R., Richey, C., Schnell, T., McKenzie, F. D., & Li, J. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.04.016 · Cited by 109 · see citing works
Hesar, H. D., & Mohebbi, M. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2582340 · Cited by 108 · see citing works
An improved Akaike information criterion for state-space model selection
Bengtsson, T., & Cavanaugh, J. E. (2005) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2005.05.003 · Cited by 105 · see citing works
Using ICA for removal of ocular artifacts in EEG recorded from blind subjects
Flexer, A., Bauer, H., Pripfl, J., & Dorffner, G. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.03.012 · Cited by 105 · see citing works
Hamaneh, M. B., Chitravas, N., Kaiboriboon, K., Lhatoo, S. D., & Loparo, K. A. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2295173 · Cited by 105 · see citing works
Artifacts removal in EEG signal using a new neural network enhanced adaptive filter
Jafarifarmand, A., & Badamchizadeh, M. A. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.09.024 · Cited by 102 · see citing works
A Nonstationary Model of Newborn EEG
Rankine, L., Stevenson, N. J., Mesbah, M., & Boashash, B. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.886667 · Cited by 98 · see citing works
Ye, Y., Cheng, Y., He, W., Hou, M., & Zhang, Z. (2016) · IEEE Sensors Journal · doi:10.1109/jsen.2016.2597265 · Cited by 94 · see citing works
Zhao, Q., Hu, B., Shi, Y., Li, Y., Moore, P. T., Sun, M., & Peng, H. (2014) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2014.2316811 · Cited by 85 · see citing works
Automated artifact removal as preprocessing refines neonatal seizure detection
Vos, M. D., Deburchgraeve, W., Cherian, P. J., Matić, V., Swarte, R. M. C., Govaert, P. P., Visser, G. H., & Huffel, S. V. (2011) · Clinical Neurophysiology · doi:10.1016/j.clinph.2011.04.026 · Cited by 84 · see citing works
Hu, J., Wang, C., Wu, M., Du, Y., He, Y. M., & She, J. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.09.040 · Cited by 84 · see citing works
Boltzmann Machine and Mean-Field Approximation for Structured Sparse Decompositions
Drémeau, A., Herzet, C., & Daudet, L. (2012) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2012.2192436 · Cited by 84 · see citing works
An Open-Source Feature Extraction Tool for the Analysis of Peripheral Physiological Data
Nabian, M., Yin, Y., Wormwood, J. B., Quigley, K. S., Barrett, L. F., & Ostadabbas, S. (2018) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2018.2878000 · Cited by 83 · see citing works
Chen, X., He, C., & Peng, H. (2014) · Journal of Applied Mathematics · doi:10.1155/2014/261347 · Cited by 78 · see citing works
Zeng, H., Song, A., Yan, R., & Qin, H. (2013) · Sensors · doi:10.3390/s131114839 · Cited by 77 · see citing works
Automatic detection of EEG artefacts arising from head movements using EEG and gyroscope signals
O’Regan, S., Faul, S., & Marnane, W. P. (2012) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2012.08.017 · Cited by 76 · see citing works
Removal of EOG artefacts by combining wavelet neural network and independent component analysis
Burger, C. N., & Vandenheever, D. (2014) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2014.09.009 · Cited by 73 · see citing works
Guerrero-Mosquera, C., & Navia-Vázquez, A. (2012) · IET Signal Processing · doi:10.1049/iet-spr.2010.0135 · Cited by 69 · see citing works
Viola, F. C., Vos, M. D., Hine, J. E., Sandmann, P., Bleeck, S., Eyles, J., & Debener, S. (2012) · Hearing Research · doi:10.1016/j.heares.2011.12.010 · Cited by 69 · see citing works
BioSigKit: A Matlab Toolbox and Interface for Analysis of BioSignals
Sedghamiz, H. (2018) · The Journal of Open Source Software · doi:10.21105/joss.00671 · Cited by 66 · see citing works
Mammone, N., & Morabito, F. C. (2014) · Entropy · doi:10.3390/e16126553 · Cited by 65 · see citing works
A fast, robust algorithm for power line interference cancellation in neural recording
Keshtkaran, M. R., & Yang, Z. Q. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/2/026017 · Cited by 64 · see citing works
Artifact suppression from EEG signals using data adaptive time domain filtering
Molla, M. K. I., Islam, M. R., Tanaka, T., & Rutkowski, T. M. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.05.009 · Cited by 61 · see citing works
Self-citationWang, Z., Xu, P., Liu, T., Tian, Y., Lei, X., & Yao, D. (2013) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2013.10.006 · Cited by 53 · see citing works
Hesar, H. D., & Mohebbi, M. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2706298 · Cited by 53 · see citing works
Artifact characterization and removal for in vivo neural recording
Islam, M. K., Rastegarnia, A., Nguyen, A. T., & Yang, Z. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2014.01.027 · Cited by 51 · see citing works
Self-citationHallez, H., Vos, M. D., Vanrumste, B., Hese, P. V., Assecondi, S., Laere, K. V., Dupont, P. J., Paesschen, W. V., Huffel, S. V., & Lemahieu, I. A. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2009.05.010 · Cited by 48 · see citing works
Efficient Variational Bayesian Approximation Method Based on Subspace Optimization
Zheng, Y., Fraysse, A., & Rodet, T. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2383321 · Cited by 47 · see citing works
Cancelling ECG Artifacts in EEG Using a Modified Independent Component Analysis Approach
Devuyst, S., Dutoit, T., Stenuit, P., Kerkhofs, M., & Stanus, E. (2008) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2008/747325 · Cited by 47 · see citing works
Rodenhauser, A., Good, W. W., Zenger, B., Tate, J. D., Aras, K., Burton, B. M., & MacLeod, R. (2018) · The Journal of Open Source Software · doi:10.21105/joss.00472 · Cited by 46 · see citing works
Clinical Neurophysiology of Infancy, Childhood, and Adolescence
(2006) · Elsevier eBooks · doi:10.1016/b978-0-7506-7251-1.x5001-1 · Cited by 44 · see citing works
Motion-Artifact Reduction in Capacitive Heart-Rate Measurements by Adaptive Filtering
Xu, L., Rabotti, C., Zhang, Y., Ouzounov, S. F., Harpe, P. J. A., & Mischi, M. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2884041 · Cited by 44 · see citing works
Using a multichannel Wiener filter to remove eye-blink artifacts from EEG data
Borowicz, A. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.05.012 · Cited by 43 · see citing works
Automatic Artefact Removal from Event-related Potentials via Clustering
Nicolaou, N., & Nasuto, S. J. (2007) · The Journal of VLSI Signal Processing Systems for Signal Image and Video Technology · doi:10.1007/s11265-006-0011-z · Cited by 42 · see citing works
Yang, B., Duan, K., & Zhang, T. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.06.067 · Cited by 41 · see citing works
A Fixed-Lag Kalman Smoother to Filter Power Line Interference in Electrocardiogram Recordings
Warmerdam, G. J. J., Vullings, R., Schmitt, L., van Laar, J. O. E. H., & Bergmans, J. W. M. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2626519 · Cited by 40 · see citing works
Artifact removal in EEG using Morphological Component Analysis
Yong, X., Ward, R. K., & Birch, G. E. (2009) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2009.4959591 · Cited by 37 · see citing works
Exercise Seismocardiography for Detection of Coronary Artery Disease
Salerno, D. M., Zanetti, J. M., Poliac, L. C., Crow, R. S., Hannan, P. J., Wang, K., Goldenberg, I. F., & Tassel, R. A. V. (2017) · American Journal of Noninvasive Cardiology · doi:10.1159/000470383 · Cited by 35 · see citing works
Recent Trends in Information and Communication Technology
(2017) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-319-59427-9 · Cited by 34 · see citing works
Real-time EEG Source-mapping Toolbox (REST): Online ICA and source localization
Pion-Tonachini, L., Hsu, S.-H., Makeig, S., Jung, T., & Cauwenberghs, G. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319299 · Cited by 32 · see citing works
Turnip, A., & Junaidi, E. (2014) · doi:10.1109/time-e.2014.7011635 · Cited by 31 · see citing works
Xiong, F., Chen, D., Chen, Z., & Dai, S. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.12.030 · Cited by 31 · see citing works
Variational Bayesian Methods For Multimedia Problems
Chen, Z., Babacan, S. D., Molina, R., & Katsaggelos, A. K. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2307692 · Cited by 29 · see citing works
ECG removal in preterm EEG combining empirical mode decomposition and adaptive filtering
Navarro‐Sune, X., Porée, F., & Carrault, G. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6287970 · Cited by 29 · see citing works
Schetinin, V., & Schult, J. (2004) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2004.824735 · Cited by 28 · see citing works
Quazi, M. H., & Kahalekar, S. G. (2017) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2017.04.003 · Cited by 28 · see citing works
Design and Study of a Smart Cup for Monitoring the Arm and Hand Activity of Stroke Patients
Bobin, M., Anastassova, M., Boukallel, M., & Ammi, M. (2018) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2018.2853553 · Cited by 28 · see citing works
Detection of artifacts from high energy bursts in neonatal EEG
Bhattacharyya, S., Biswas, A., Mukherjee, J., Majumdar, A. K., Majumdar, B., Mukherjee, S., & Singh, A. K. (2013) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2013.07.031 · Cited by 25 · see citing works
Calcagno, S. (2014) · American Journal of Applied Sciences · doi:10.3844/ajassp.2014.57.68 · Cited by 24 · see citing works
A fully automatic ocular artifact removal from EEG based on fourth-order tensor method
Ge, S., Han, M., & Hong, X. (2014) · Biomedical Engineering Letters · doi:10.1007/s13534-014-0118-2 · Cited by 23 · see citing works
Kim, C. S., Sun, J., Liu, D., Wang, Q., & Paek, S. (2017) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2017.7510370 · Cited by 22 · see citing works
Kabir, A.-U., Shahin, F. B., & Islam, M. K. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1487/1/012043 · Cited by 21 · see citing works
Self-citationZhao, C., & Qiu, T. (2011) · doi:10.1109/iembs.2011.6091040 · Cited by 19 · see citing works
Removal of eye blinking artifacts from EEG incorporating a new constrained BSS algorithm
Shoker, L., Sanei, S., & Latif, M. A. (2005) · doi:10.1109/iembs.2004.1403307 · Cited by 18 · see citing works
Self-citationWavelet Enhanced Cca For Minimization Of Ocular And Muscle Artifacts In Eeg
Raghavendra, B. S., & Dutt, D. N. (2011) · doi:10.5281/zenodo.1075223 · Cited by 16 · see citing works
Newborn EEG Seizure Simulation Using Time-Frequency Signal Synthesis
Stevenson, N. J., Rankine, L., Mesbah, M., & Boashash, B. (2005) · Cited by 14 · see citing works
Automatic artifacts removal of EEG signals using robust principal component analysis
Turnip, A. (2014) · doi:10.1109/time-e.2014.7011641 · Cited by 14 · see citing works
Variational Bayes for Spatiotemporal Identification of Event-Related Potential Subcomponents
Mohseni, H. R., Ghaderi, F., Wilding, E. L., & Sanei, S. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2050318 · Cited by 12 · see citing works
Self-citationRoy, V., & Shukla, S. (2014) · International Journal of Modern Education and Computer Science · doi:10.5815/ijmecs.2014.07.05 · Cited by 11 · see citing works
Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep LearningPreprint
Schwabedal, J. T. C., Sippel, D., Brandt, M., & Bialonski, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.08443 · Cited by 9 · see citing works
Artifact suppression and analysis of brain activities with electroencephalography signals.
Rashed-Al-Mahfuz, M., Islam, M. R., Hirose, K., & Molla, M. K. I. (2013) · PubMed Central · doi:10.3969/j.issn.1673-5374.2013.16.007 · Cited by 8 · see citing works
Self-citationAnalysis and processing of in-vivo neural signal for artifact detection and removal
Islam, M. K., Tuan, N. A., Zhou, Y., & Yang, Z. (2012) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2012.6513197 · Cited by 7 · see citing works
Self-citationThe power of tensor decompositions in biomedical applications
Hunyadi, B., Huffel, S. V., & Vos, M. D. (2016) · Institution of Engineering and Technology eBooks · doi:10.1049/pbhe002e_ch5 · Cited by 5 · see citing works
Ghaleb, F. A. A., Kamat, M. B., Salleh, M., Rohani, M. F., & Hadji, S. E. (2017) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-319-59427-9_13 · Cited by 5 · see citing works
Artefact Detection and Removal Algorithms for EEG Diagnostic SystemsPreprint
O’Regan, S. (2018) · doi:10.31237/osf.io/95x6f · Cited by 4 · see citing works
Spatial correlation based artifact detection for automatic seizure detection in EEG
Skupch, A. M., Dollfuss, P., Fürbass, F., Gritsch, G., Hartmann, M., Perko, H., Pataraia, E., Lindinger, G., & Kluge, T. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6609915 · Cited by 3 · see citing works
Flagged first, then by citations
2 of the 116 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23020828 · Cited by 120 · see citing works
Data quality evaluation in wearable monitoring
Böttcher, S., Vieluf, S., Bruno, E., Joseph, B., Epitashvili, N., Biondi, A., Zabler, N., Glasstetter, M., Dümpelmann, M., Van Laerhoven, K., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-25949-x · Cited by 91 · see citing works
Martínek, R., Ládrová, M., Šidiková, M., Jaroš, R., Behbehani, K., Kahankova, R. V., & Kawala‐Sterniuk, A. (2021) · Sensors · doi:10.3390/s21155186 · Cited by 86 · see citing works
“Broadband signals [41,55] Myopotentials and motion artifacts 20 Hz–10 kHz up to 10 mV muscle activity, bulk movements/exercise”
BackgroundSmart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2022) · Sensors · doi:10.3390/s22197472 · Cited by 62 · see citing works
“[136,138–140].”
BackgroundTuncer, T., Doğan, Ş., Bayğın, M., Taşçı, İ., Mungen, B., Taşçı, B., Barua, P. D., & Acharya, U. R. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112555 · Cited by 41 · see citing works
On Smart Geometric Non-Destructive Evaluation: Inspection Methods, Overview, and Challenges
Jaber, A., Karganroudi, S. S., Meiabadi, M. S., Aminzadeh, A., Ibrahim, H., Adda, M., & Taheri, H. (2022) · Materials · doi:10.3390/ma15207187 · Cited by 38 · see citing works
Jiang, T., Fang, Y., Zheng, N., & Chen, J. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105674 · Cited by 30 · see citing works
Reshaping the healthcare world by AI-integrated wearable sensors following COVID-19
Khan, B., Khalid, R. T., Wara, K. U., Masrur, M. A., Khan, S., Khan, W. U., Amara, U., & Abdullah, S. (2025) · Chemical Engineering Journal · doi:10.1016/j.cej.2025.159478 · Cited by 26 · see citing works
Biosignals meet Adaptive Systems
Schultz, T., & Maedche, A. (2023) · SN Applied Sciences · doi:10.1007/s42452-023-05412-w · Cited by 22 · see citing works
Alqudah, A. M., & Moussavi, Z. (2025) · Applied Sciences · doi:10.3390/app152212036 · Cited by 29 · see citing works
LTDNet-EEG: A Lightweight Network of Portable/Wearable Devices for Real-Time EEG Signal Denoising
Huang, J., Wang, C., Zhao, W., Grau, A., Xue, X., & Zhang, F. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3412774 · Cited by 20 · see citing works
Besheli, B. F., Sha, Z., Gavvala, J. R., Gürses, C., Karamürsel, S., Quach, M. M., Curry, D. J., Sheth, S. A., Francis, D. J., Henry, T. R., et al. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac8766 · Cited by 19 · see citing works
García-Murillo, D. G., Álvarez-Meza, A. M., & Castellanos-Dominguez, G. (2021) · Sensors · doi:10.3390/s21082750 · Cited by 16 · see citing works
“Namely, while spatial filters are efficient on clean datasets that were obtained in constrained environments, they need a convenient artifact removing from EEG signals because of distorting artifacts and outliers of real-world acquisition contexts [17], the interval of neural responses is generally chosen heuristicall…”
BackgroundGhosh, S., Sindhujaa, P., Kesavan, D. K., Gulyás, B. Z., & Máthé, D. (2025) · Surgeries · doi:10.3390/surgeries6030050 · Cited by 15 · see citing works
“Common techniques include adaptive filtering, independent component analysis (ICA), and wavelet transforms, which help mitigate interference from muscle movements, eye blinks, and environmental electrical noise [62].”
Automated Detection and Removal of Cardiac and Pulse Interferences from Neonatal EEG Signals
Tamburro, G., Croce, P., Zappasodi, F., & Comani, S. (2021) · Sensors · doi:10.3390/s21196364 · Cited by 13 · see citing works
Noise Removal of ECG signal using Multi-Techniques
Marzog, H. A., Abd, H. J., & Yonis, A. Z. (2022) · IEEE Integrated STEM Education Conference (ISEC) · doi:10.1109/isec54952.2022.10025094 · Cited by 10 · see citing works
Ferreira, A. F., Alves, H., Silva, H., Marques, N., & Fred, A. L. N. (2024) · Scientific Reports · doi:10.1038/s41598-024-56733-8 · Cited by 9 · see citing works
Research challenges and future directions towards medical data processing
Ampavathi, A., & Thommandru, V. S. (2022) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2021.2018665 · Cited by 9 · see citing works
“Islam et al. (2021) has introduced different sources of artefacts and noises present in bio signal recordings, such as EEG, ECG and EMG. Faiyaz et al. (2021) has addressed block chain can be integrated to protect data from tampering, maintain data integrity, and allow reliable access, distributed…”
BackgroundSignal Processing Techniques for Computational Health Informatics
Humeau‐Heurtier, A. (2020) · Intelligent systems reference library · doi:10.1007/978-3-030-54932-9 · Cited by 9 · see citing works
Video Object Detection for Privacy-Preserving Patient Monitoring in Intensive Care
Emberger, R., Boss, J. M., Baumann, D., Seric, M., Huo, S., Tuggener, L., Keller, E., & Stadelmann, T. (2023) · IEEE Swiss Conference on Data Science (SDS) · doi:10.1109/sds57534.2023.00019 · Cited by 8 · see citing works
“However, without appropriate contextual knowledge, it is not possible to correctly interpret biosignals and distinguish physiological features from artifacts, even though many domain-specific signal processing techniques have been developed [3].”
BackgroundGeneralized Label-Free Artefact Cleaning for Real-Time Medical Pulsatile Time Series
Chen, X., Olakorede, I., Bögli, S. Y., Xu, W., Beqiri, E., Li, X., Tang, C., Gao, Z., Gao, S., Ercole, A., et al. (2026) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2026.3655095 · Cited by 7 · see citing works
“Recentadvances inmachinelearningartefactcleaningmethodshaveshownsu-periorperformancecomparedtothetraditionalstatisticaland signalprocessingmethods[5],[6],[7],particularlyinaddressing thechallengesposedbynon-stationarysignals.”
Cai, X., Xu, Z., Liu, J., Wang, R., & Wu, Y. (2024) · Engineering · doi:10.1016/j.eng.2024.12.012 · Cited by 7 · see citing works
Brain-inspired computing based on deep learning for human-computer interaction: A review
Yu, B., Zhang, S., Zhou, L., Wei, J., Sun, L., & Bu, L. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130928 · Cited by 6 · see citing works
An efficient approach for denoising EOG artifact through optimal wavelet selection
Prakash, V., & Kumar, D. (2023) · International Journal of Information Technology · doi:10.1007/s41870-023-01615-x · Cited by 5 · see citing works
Zhang, P., Naeem, K., Sarcinelli, E., Venketeswaran, A., Bukka, S., Lalam, N. R., Wright, R. F., & Ohodnicki, P. R. (2024) · Optical Waveguide and Laser Sensors · doi:10.1117/12.3013996 · Cited by 4 · see citing works
Evaluating FPGA-based denoising techniques for improved signal quality in electrocardiograms
Keerthiga, G., & Kumar, S. P. (2024) · Analog Integrated Circuits and Signal Processing · doi:10.1007/s10470-024-02277-w · Cited by 4 · see citing works
Branan, K. L., Kurian, R., McMurray, J. P., Erraguntla, M., Gutiérrez‐Osuna, R., & Coté, G. L. (2025) · Biosensors · doi:10.3390/bios15080493 · Cited by 3 · see citing works
Recent Advancements in Online Ocular Artifacts Removal in EEG based BCI: A Review
Gudikandula, N., Janapati, R., Sengupta, R., & Chintala, S. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10724228 · Cited by 3 · see citing works
“Year Method A. Schloegl,[25] 2009 Regression R. Kher,28 2016 LMS algorithm A. Borowicz,[29] 2018 Linear filtering, Wiener filte A. Jafarifarmand,[32] 2017 ICA-ANC algorithm A. Acharyya[33] 2018 Haar wavelet decomposition ] M. Mathe[24] 2022 BSS-CCA X. Chen[35] 2017 SOS or HOS methods.”
BackgroundMethodPhonocardiogram Signal Analysis for Automated Cardiac Murmur Detection
Hangaragi, S., & Neelima, N. (2024) · International Conference on Innovations in Communications, Electrical and Computer Engineering (ICICEC) · doi:10.1109/icicec62498.2024.10808223 · Cited by 2 · see citing works
“By eliminating noise and extracting relevant features, preprocessing lays the pathway for robust diagnostic algorithms [4-6].”
EEG and fMRI Artifact Detection Techniques: A Survey of Recent Developments
Mili, R., Bouaziz, B., Maalel, A., & Gargouri, F. (2023) · SN Computer Science · doi:10.1007/s42979-023-01959-y · Cited by 2 · see citing works
Erofeev, A., Vinokurov, E. K., Bolshakova, A. V., Борисович, Б. И., & Власова, О. Л. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1550673 · Cited by 1 · see citing works
A Novel Approach to EEG Artifact Removal Using ADASYN and Optimized Hierarchical 1D CNN
Kokate, A. A., & Jadhav, T. R. (2025) · Informatics and Automation · doi:10.15622/ia.24.5.6 · Cited by 1 · see citing works
Diaz, S. F. S., & Huaman-Levano, L. A. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11252842 · Cited by 1 · see citing works
“First, a Butterworth band-pass filter composed of four biquad stages with predefined coefficients, designed from a mathematical model based on Infinite Impulse Response (IIR) filters [19], is applied.”
Thakur, S., Thakur, S., Rana, A., Kumar, P., & Das, A. K. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2602691 · Cited by 1 · see citing works
Low-Power Tunable Band-Pass Filter Using Unipolar Oxide TFT Technology for Wearable Applications
Choudhary, V., Shrivastava, S., & Bahubalindruni, P. G. (2026) · International Conference on VLSI Design · doi:10.1109/vlsid68508.2026.00092 · Cited by 1 · see citing works
“Typical bio-signals such as electro-cardiograms (ECGs), electromyograms (EMGs), and photo-plethysmograms (PPGs) occupy narrow frequency ranges, while being contaminated by motion artifacts, ambient electromagnetic interference, and neighboring physiological activity [6]–[9].”
Nadj, M., Maedche, A., Riedl, R., Seitz, J., & Schultz, T. (2026) · Electronic Markets · doi:10.1007/s12525-026-00895-y · Cited by 1 · see citing works
“…environmental (e.g., signal interference, lighting conditions), and technical (e.g., sensor malfunction) factors that introduce noise into the data (Islam et al., 2021); sensor calibration requirements (e.g., for eye tracking); and the collection of baseline data required for normalization (see…”
Video object detection for privacy-preserving patient monitoring in intensive care
Emberger, R., Boss, J. M., Baumann, D., Seric, M., Huo, S., Tuggener, L., Keller, E., & Stadelmann, T. (2023) · Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences) · doi:10.21256/zhaw-27810 · Cited by 1 · see citing works
Ocular Artefact Removal from Electroencephalogram Signals: A Review
Babeetha, S., & Sridhar, S. S. (2023) · International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) · doi:10.1109/icccis60361.2023.10425613 · Cited by 1 · see citing works
“The EEMD-CCA [34] works in a similar fashion to the EEMD-ICA in that it eliminates the polluted IMFs and then uses the unmixing matrix W, which is calculated by the CCA method, to reconstruct the original source signals.”
BackgroundMethodArtifact Detection and Removal in EEG: A Review of Methods and Contemporary Usage
Prakash, V., & Kumar, D. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-2508-3_19 · Cited by 1 · see citing works
Fundamentals of Bio-Signal Sensor Design and Development in Medical Applications
Shahbazi, A. H., Afsarimanesh, N., Tan, T., Sabet, G. S., & Lee, G. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-3-031-29871-4_44
Effective signal denoising with adaptive polynomials
Idirizov, B. (2024) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2910/1/012034
Innovations in quantitative rapid testing: Early prediction of health risks
Alleilem, K. S., Almousa, S., Alissa, M., Alrumaihi, F., Alharbi, H. O., Almansour, N. M., Aldaiji, L. A., Abouzied, A. S., Alsugoor, M. H., Alasmari, O., et al. (2025) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2025.103000
Exploring Synergies in Brain-Machine Interfaces: Compression vs. Performance
Cubillos, L. H., Kelberman, M. M., Mender, M. J., Hite, A., Temmar, H., Willsey, M. S., Kumar, N. G., Kung, T. A., Patil, P. G., Chestek, C. A., et al. (2026) · Restorative Neurology and Neuroscience · doi:10.1177/09226028261457824
“Previous research has shown that removing dimensions of brain data (in modalities such as EEG or MEG) can help in decoding by eliminating noise (Winkler et al., 2011; Islam et al., 2021) (a.k.a., denoising).”
Analysis of the Effects of EMG Sensor Circuitries On Output Signal Quality
Arefin, W., Juel, M. A. A., Islam, M. R., Tarequzzaman, M., & Moznuzzaman, M. (2025) · International Conference on Electrical, Computer and Communication Engineering · doi:10.1109/ecce64574.2025.11013207
Self-citationDesign of FPGA-based Digital Filter for Electroencephalogram Signal Processing
Chowdhury, A., Foysal, M., Chowdhury, F. E., Chowdhury, M., Das, D., & Chowdhury, M. H. (2024) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit64611.2024.11022355
Mili, R., Khaskhoussy, R., Maalel, A., Bouaziz, B., & Gargouri, F. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-78922-9_10
A Wide Range Tunable Active Band Pass Filter Using Flexible Oxide TFTs Technology
Shrivastava, S., Choudhary, V., & Bahubalindruni, P. G. (2025) · Conference on Ph.D. Research in Microelectronics and Electronics · doi:10.1109/prime66228.2025.11203426
“Signal-to-noise ratio (SNR) enhancement is paramount in biomedical signal monitoring [1]–[4].”
Navigating Noise: Strategies for EEG Artefact Removal
Rakhmatulin, I., & Naik, G. R. (2026) · Brain informatics and health · doi:10.1007/978-981-95-4177-5_7
Sahoo, S., & Jain, P. K. (2026) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-026-01741-3
“Several studies have been proposed in the literature to address this challenge, including adaptive filters [ 9], modified cosine transform-based Long short-term memory mask - ing [10], and algorithm-based motion artefact removal [11].”
Dynamic Weighted Spatio-Temporal-Spectral Fusion for EEG-Based Social Anxiety Detection
Sun, W., Xiao, G., Liu, X. Y., Gao, J., Song, S., & Zheng, Y. (2026) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2026.3702831
A new transition network construction method and characterisation for application in signal analysis
Mi, Y., Lin, A., & Hu, W. (2026) · Nonlinear Dynamics · doi:10.1007/s11071-026-12718-y
“The signal was first filtered with a band-pass filter to remove noise interference and motion artifacts [47].”
Explainable AI for Sensor-Based Medical Diagnostics
Isa, A., Boukari, S., Musa, K. I., & Aliyu, M. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-19543-2_5
EEG-Based Eye State Recognition via Multi-domain Feature Engineering and Ensemble Learning
Qiu, M., Wei, Y., & TaheriNejad, N. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29839-3_15
Zibandehpoor, M., & Delrobaei, M. (2026) · IEEE Access · doi:10.1109/access.2026.3727162
“Arti-facts and signal jumps from transient sensor misalignment were identified using an adaptive threshold ( ± 3 standard deviations from the moving average) and corrected through cumulative offset adjustment [49].”
- Authors
- Islam, M. K., Rastegarnia, A., & Sanei, S.
- Affiliations
- Independent University, Bangladesh (BD)
- Malayer University (IR)
- Nottingham Trent University (GB)
- Corresponding author
- Islam, Md. Kafiul
- Venue
- Intelligent Systems Reference Library
- Publisher
- Springer International Publishing
- Published
- 2020-10-08
- Pages
- 23-79
- ISSN
- 1868-4394 (ISSN-L)
- 1868-4408
- OpenAlex
- W3092321252
- Cited by
- 54
- References
- 116
- Citations by year
- 2026: 12 · 2025: 12 · 2024: 12 · 2023: 8 · 2022: 6 · 2021: 3 · 2020: 1
- FWCI
- 13.10
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Intelligent systems reference library (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Computer Science › Signal Processing › Blind Source Separation Techniques
- Keywords
- noise reduction, EEG, ECG, electromyography, brain-computer interface, neural prostheses, artifact detection, real-time signal processing, low sampling rate, computational complexity, diagnosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review