Signal Artifacts and Techniques for Artifacts and Noise Removal

EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

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

FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

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

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

(2011) · doi:10.1109/embc13666.2011 · Cited by 1,707 · see citing works

Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects

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

A practical guide to the selection of independent components of the electroencephalogram for artifact correction

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

Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis

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

The 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

The Use of Ensemble Empirical Mode Decomposition With Canonical Correlation Analysis as a Novel Artifact Removal Technique

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

Temporally constrained ica: an application to artifact rejection in electromagnetic brain signal analysis

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

Unsupervised Eye Blink Artifact Denoising of EEG Data with Modified Multiscale Sample Entropy, Kurtosis, and Wavelet-ICA

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

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 221 · 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 219 · 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

Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data

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

REG-ICA: A hybrid methodology combining Blind Source Separation and regression techniques for the rejection of ocular artifacts

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

Automatic 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

Removal of Ocular Artifacts in EEG—An Improved Approach Combining DWT and ANC for Portable Applications

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

Detection and classification of subject-generated artifacts in EEG signals using autoregressive models

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

ANSLAB: 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

ECG Denoising Using Marginalized Particle Extended Kalman Filter With an Automatic Particle Weighting Strategy

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

Automated Removal of EKG Artifact From EEG Data Using Independent Component Analysis and Continuous Wavelet Transformation

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

Combining Nonlinear Adaptive Filtering and Signal Decomposition for Motion Artifact Removal in Wearable Photoplethysmography

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

Automatic Identification and Removal of Ocular Artifacts in EEG—Improved Adaptive Predictor Filtering for Portable Applications

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

Removal of EOG and EMG artifacts from EEG using combination of functional link neural network and adaptive neural fuzzy inference system

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

Removal of Muscle Artifacts from Single-Channel EEG Based on Ensemble Empirical Mode Decomposition and Multiset Canonical Correlation Analysis

Chen, X., He, C., & Peng, H. (2014) · Journal of Applied Mathematics · doi:10.1155/2014/261347 · Cited by 78 · see citing works

EOG Artifact Correction from EEG Recording Using Stationary Subspace Analysis and Empirical Mode Decomposition

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

Automatic removal of ocular artefacts using adaptive filtering and independent component analysis for electroencephalogram data

Guerrero-Mosquera, C., & Navia-Vázquez, A. (2012) · IET Signal Processing · doi:10.1049/iet-spr.2010.0135 · Cited by 69 · see citing works

Semi-automatic attenuation of cochlear implant artifacts for the evaluation of late auditory evoked potentials

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

Enhanced Automatic Wavelet Independent Component Analysis for Electroencephalographic Artifact Removal

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

Robust removal of ocular artifacts by combining Independent Component Analysis and system identification

Wang, 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

An Adaptive Particle Weighting Strategy for ECG Denoising Using Marginalized Particle Extended Kalman Filter: An Evaluation in Arrhythmia Contexts

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

Removing muscle and eye artifacts using blind source separation techniques in ictal EEG source imaging

Hallez, 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

PFEIFER: Preprocessing Framework for Electrograms Intermittently Fiducialized from Experimental Recordings

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

Removal of EOG artifacts from EEG using a cascade of sparse autoencoder and recursive least squares adaptive filter

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

Removal artifacts from EEG signal using independent component analysis and principal component analysis

Turnip, A., & Junaidi, E. (2014) · doi:10.1109/time-e.2014.7011635 · Cited by 31 · see citing works

Cancellation of motion artifacts in ambulatory ECG signals using TD-LMS adaptive filtering techniques

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

The Combined Technique for Detection of Artifacts in Clinical Electroencephalograms of Sleeping Newborns

Schetinin, V., & Schult, J. (2004) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2004.824735 · Cited by 28 · see citing works

Artifacts removal from EEG signal: FLM optimization-based learning algorithm for neural network-enhanced adaptive filtering

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

INDEPENDENT COMPONENT ANALYSIS AND DISCRETE WAVELET TRANSFORM FOR ARTIFACT REMOVAL IN BIOMEDICAL SIGNAL PROCESSING

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

Notice of Removal: Removal of ocular artifacts using ICA and adaptive filter for motor imagery-based BCI

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

Design and Implementation of an EOG-based Mouse Cursor Control for Application in Human-Computer Interaction

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

An automatic ocular artifacts removal method based on wavelet-enhanced canonical correlation analysis

Zhao, 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

Wavelet 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

Automatic Removal of Artifacts from EEG Signal based on Spatially Constrained ICA using Daubechies Wavelet

Roy, 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 11 · 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

Analysis 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

The 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

Motion Artifact Reduction Algorithm Using Sequential Adaptive Noise Filters and Estimation Methods for Mobile ECG

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

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

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part I: Cardiac Signals

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

Smart 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 66 · see citing works

Directed Lobish-based explainable feature engineering model with TTPat and CWINCA for EEG artifact classification

Tuncer, 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 40 · see citing works

Understanding construction workers’ cognitive processes under risky scenarios through electroencephalography

Jiang, T., Fang, Y., Zheng, N., & Chen, J. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105674 · Cited by 31 · 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

Bridging Signal Intelligence and Clinical Insight: A Comprehensive Review of Feature Engineering, Model Interpretability, and Machine Learning in Biomedical Signal Analysis

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

A sparse representation strategy to eliminate pseudo-HFO events from intracranial EEG for seizure onset zone localization

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

Single-Trial Kernel-Based Functional Connectivity for Enhanced Feature Extraction in Motor-Related Tasks

García-Murillo, D. G., Álvarez-Meza, A. M., & Castellanos-Dominguez, G. (2021) · Sensors · doi:10.3390/s21082750 · Cited by 16 · see citing works

Brain-Computer Interfaces and AI Segmentation in Neurosurgery: A Systematic Review of Integrated Precision Approaches

Ghosh, S., Sindhujaa, P., Kesavan, D. K., Gulyás, B. Z., & Máthé, D. (2025) · Surgeries · doi:10.3390/surgeries6030050 · Cited by 15 · see citing works

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

Exploring the electrical robustness of conductive textile fasteners for wearable devices in different human motion conditions

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 10 · 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

Signal 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

Generalized 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

Recent Advances in Intracortical Neural Interfaces for Freely Moving Animals: Technologies and Applications

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

Investigation of data augmentation techniques for ultrasonic acoustic fiber sensing signals in guided wave-based pipeline damage detection

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

Robust Prediction of Cardiorespiratory Signals from a Multimodal Physiological System on the Upper Arm

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

Phonocardiogram 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

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

Disrupted calcium dynamics and electrophysiological activity in the stratum pyramidale and hippocampal alveus during fear conditioning in the 5xFAD model of Alzheimer’s disease

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

Makita Hapiy 2.0: Design and Development of a Low-Cost and Lightweight Myoelectric Transradial Prosthesis using 3D Printing

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

A Systematic Review of Techniques, Artifacts, Challenges and Future Directions on Signal Acquisition and Preprocessing in Brain-Computer Interfaces (BCIs)

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

Biosignal-adaptive platforms

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

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

Artifact 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

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

Design 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

Eyebrow, Blink and Head Movement Artifacts Detection from EEG Signals Using Machine Learning Techniques

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

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

A triaxial accelerometer-based approach for motion noise resilient segmentation of seismocardiogram signal

Sahoo, S., & Jain, P. K. (2026) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-026-01741-3

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

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

Classifying Spatial Navigation Performance in Healthy Adults Using Multimodal Behavioral Analysis of the Walking Corsi Test

Zibandehpoor, M., & Delrobaei, M. (2026) · IEEE Access · doi:10.1109/access.2026.3727162

ARTIFACT REMOVAL FROM EEG RECORDINGS - AN OVERVIEW

Dhiman, R. (2010) · Cited by 10 · see citing works

Signal Preprocessing and Artifact Removal for Brain-Computer Interfaces

Damian, M.-M., & Precup, R. (2022) · IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI) · doi:10.1109/saci55618.2022.9919582

Brain Computer Interface: EEG Signal Preprocessing Issues and Solutions

ElSayed, Z., Zaghloul, Z. S., & Bayoumi, M. (2017) · International Journal of Computer Applications · doi:10.5120/ijca2017914621 · Cited by 52 · see citing works

Advanced Signal Processing and Machine/Deep Learning Approaches on a Preprocessing Block for EEG Artifact Removal: A Comprehensive Review

Agounad, S., Tarahi, O., Moufassih, M., Hamou, S., & Mazid, A. (2024) · Circuits Systems and Signal Processing · doi:10.1007/s00034-024-02936-3 · Cited by 23 · see citing works

Methods of Automatic Artifact Removal in Neurobiological Signals

Ciecierski, K. A. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-77179-3_7 · Cited by 2 · see citing works