Cited by 810Blind Source Separation TechniquesEEG and Brain-Computer InterfacesTensor decomposition and applications

The DOI matches its Crossref record; 56 of the 57 references it cites were found in a registry or an index.

Independent EEG Sources Are Dipolar

Open access

Delorme, Arnaud, Palmer, Jason, Onton, Julie, Oostenveld, Robert, Makeig, Scott·2012·PLoS ONE

DOI 10.1371/journal.pone.0030135PMC PMC3280242

Open this paper in CoChat — free account Open PDF

Abstract

Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition 'dipolarity' defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison).

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1371/journal.pone.0030135

Cites

56 of 56 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
Self-citation
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Independent component analysis: algorithms and applications
Hyvärinen, A., & Erkki Oja, A. (2000) · Neural Networks · doi:10.1016/s0893-6080(00)00026-5 · Cited by 8,926
Independent component analysis, A new concept?
Comon, P. (1994) · Signal Processing · doi:10.1016/0165-1684(94)90029-9 · Cited by 8,324
The brainweb: phase synchronization and large-scale integration
Varela, F., Lachaux, J.-P., Rodriguez, E., & Martinerie, J. (2001) · Nature Reviews Neuroscience · doi:10.1038/35067550 · Cited by 4,761
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
Self-citation
Neuronal Avalanches in Neocortical Circuits
Beggs, J. M., & Plenz, D. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-35-11167.2003 · Cited by 2,325
Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources
Lee, T.-W., Girolami, M., & Sejnowski, T. J. (1999) · Neural Computation · doi:10.1162/089976699300016719 · Cited by 1,967
Independent Component Analysis of Electroencephalographic Data
Makeig, S., Bell, A. J., Jung, T., & B, T. J. S. A. (1995) · Cited by 1,957
Self-citation
Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis
Delorme, A., Sejnowski, T., & Makeig, S. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.11.004 · Cited by 1,759
Self-citation

Flagged first, then by citations

Show all 57 references

1 of the 57 references couldn’t be matched to a known paper.

Research works printed but not linked: 2

G Gómez-Herrero, 2008, Gómez-Herrero G, Atienza M, Egiazarian K, Cantero JL., Neuroimage, 43, 497
Z Akalin Acar, 2011, Adaptive mixture independent component analysis and source localization in epilepsy., IEEE EMBC

Sorted by citations

Items 401–500 of 810

A Bayesian framework for unifying data cleaning, source separation and imaging of electroencephalographic signalsPreprint
Ojeda, A., Klug, M., Kreutz-Delgado, K., Gramann, K., & Mishra, J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/559450 · Cited by 13
Neuroplasticity of the left dorsolateral prefrontal cortex in patients with treatment-resistant depression as indexed with paired associative stimulation: a TMS–EEG study
Kaneko, N., Wada, M., Nakajima, S., Takano, M., Taniguchi, K., Honda, S., Mimura, M., & Noda, Y. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhad515 · Cited by 13
Causal analysis of cortical networks involved in reaching to spatial targets
Iversen, J. R., Ojeda, A., Mullen, T. R., Plank, M., Snider, J., Cauwenberghs, G., & Howard Poizner, A. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944599 · Cited by 12
Cortical networks for reference-frame processing are shared by language and spatial navigation systems
Vukovic, N., & Юрьевич, Ш. Ю. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.041 · Cited by 13
Modulation of Frontal Oscillatory Power during Blink Suppression in Children: Effects of Premonitory Urge and Reward
Miyakoshi, M., Jurgiel, J., Dillon, A., Chang, S. W., Piacentini, J. C., Makeig, S., & Loo, S. K. (2020) · Cerebral Cortex Communications · doi:10.1093/texcom/tgaa046 · Cited by 14
Self-citation
The bibliometric analysis of EEGLAB software in the Web of Science indexed articles
Fayaz, M. (2023) · Neuroscience Informatics · doi:10.1016/j.neuri.2023.100154 · Cited by 14
Investigation of the changes in oscillatory power during task switching after mild traumatic brain injury
Barlow, S. E., Medrano, P., Seichepine, D. R., & Ross, R. S. (2018) · European Journal of Neuroscience · doi:10.1111/ejn.14231 · Cited by 15
Granger Causality-Based Pain Classification Using EEG Evoked by Electrical Stimulation Targeting Nociceptive Aδ and C Fibers
Tripanpitak, K., He, S., Sonmezisik, I., Morant, T., Huang, S. Y., & Yu, W. (2021) · IEEE Access · doi:10.1109/access.2021.3050302 · Cited by 12
Effects of Transcranial Direct Current Stimulation (tDCS) on Go/NoGo Performance Using Food and Non-Food Stimuli in Patients with Prader–Willi Syndrome
Poje, A. B., Manzardo, A. M., Gustafson, K. M., Liao, K., Martin, L. E., & Butler, M. G. (2021) · Brain Sciences · doi:10.3390/brainsci11020250 · Cited by 12
PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG
Ball, K., Bigdely-Shamlo, N., Mullen, T. R., & Robbins, K. A. (2016) · Computational Intelligence and Neuroscience · doi:10.1155/2016/9754813 · Cited by 12
High Accuracy EEG Biometrics Identification Using ICA and AR Model
Kaewwit, C., Lursinsap, C., & Sophatsathit, P. (2017) · Journal of Information and Communication Technology · doi:10.32890/jict2017.16.2.8 · Cited by 12
Do Individuals With Obsessive-Compulsive Disorder and Obsessive-Compulsive Personality Disorder Share Similar Neural Mechanisms of Decision-Making Under Ambiguous Circumstances?
Luo, Y., Chen, L., Li, H., Dong, Y., Zhou, X., Qiu, L., Zhang, L., Gao, Y., Zhu, C., Yu, F., et al. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.585086 · Cited by 11
Unified Coding of Spectral and Temporal Phonetic Cues: Electrophysiological Evidence for Abstract Phonological Features
Monahan, P. J., Schertz, J. L., Fu, Z., & Pérez, A. (2022) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01817 · Cited by 12
Investigating neural dynamics in autism spectrum conditions outside of the laboratory using mobile electroencephalography
Giannadou, A., Jones, M., Freeth, M., Samson, A. C., & Milne, E. (2022) · Psychophysiology · doi:10.1111/psyp.13995 · Cited by 11
Neural signature of everyday function in older adults at-risk of cognitive impairment
Sanctis, P. D., Wagner, J., Molholm, S., Foxe, J. J., Blumen, H. M., & Horsthuis, D. J. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.11.005 · Cited by 12
A Hybrid EEG-Based Stress State Classification Model Using Multi-Domain Transfer Entropy and PCANet
Dong, Y., Xu, L., Zheng, J., Wu, D., Li, H., Shao, Y., Shi, G., & Fu, W. (2024) · Brain Sciences · doi:10.3390/brainsci14060595 · Cited by 11
Independent component analysis combined with compressed sensing for EEG compression in BCI
Zhou, B., Wu, X., Lv, Z., Zhang, L., & Zhang, C. (2015) · International Conference on Information, Communications & Signal Processing (ICICS) · doi:10.1109/icics.2015.7459902 · Cited by 11
Sources of electrophysiological and foci of hemodynamic brain activity most relevant for controlling a hybrid brain–Computer interface based on classification of EEG patterns and near-infrared spectrography signals during motor imagery
Дмитриевич, Б. П., Isaev, M. R., Korshakov, A., Oganesyan, V. V., Kerechanin, J., Popodko, A. I., & Frolov, A. (2016) · Human Physiology · doi:10.1134/s036211971603004x · Cited by 11
Enhancement of Movement Intention Detection Using EEG Signals Responsive to Emotional Music Stimulus
Hasan, S. M. S., Siddiquee, M. R., Marquez, J. S., & Bai, O. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3025004 · Cited by 11
On the interpretation of linear Riemannian tangent space model parameters in M/EEG
Kobler, R. J., Hirayama, J.-I., Hehenberger, L., Lopes-Dias, C., Muller-Putz, G. R., & Kawanabe, M. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630144 · Cited by 11
Spatio-temporal reconstruction of brain dynamics from EEG with a Markov prior
Hansen, S. T., & Hansen, L. K. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.030 · Cited by 11
As above, so below? Towards understanding inverse models in BCI
Lindgren, J. T. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa86d0 · Cited by 9
HIGH ACCURACY EEG BIOMETRICS IDENTIFICATION USING ICA AND AR MODEL
Kaewwit, C., Lursinsap, C., & Sophatsathit, P. (2017) · Journal of Information and Communication Technology · doi:10.32890/jict2017.16.2.8236 · Cited by 11
Motor Unit-Driven Identification of Pathological Tremor in Electroencephalograms
Holobar, A., Gallego, J. Á., Kranjec, J., Rocón, E., Romero, J. P., Benito‐León, J., Pons, J. L., & Glaser, V. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00879 · Cited by 11
Sources of the frontocentral mismatch negativity and P3a responses in schizophrenia patients and healthy comparison subjects
Koshiyama, D., Miyakoshi, M., Joshi, Y. B., Nakanishi, M., Tanaka‐Koshiyama, K., Sprock, J., & Light, G. A. (2021) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2021.01.005 · Cited by 11
Altered Cortical Activation Associated with Mirror Overflow Driven by Non-Dominant Hand Movement in Attention-Deficit/Hyperactivity Disorder
Luo, Y., Chen, C. I., Adamek, J. H., Crocetti, D., Mostofsky, S. H., & Ewen, J. B. (2021) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2021.110433 · Cited by 11
Musical and linguistic syntactic processing in agrammatic aphasia: An ERP study
Chiappetta, B., Patel, A. D., & Thompson, C. K. (2021) · Journal of Neurolinguistics · doi:10.1016/j.jneuroling.2021.101043 · Cited by 10
Interictal EEG noise cancellation: GEVD and DSS based approaches versus ICA and DCCA based methods
Sardouie, S. H., Shamsollahi, M. B., Albera, L., & Merlet, I. (2014) · IRBM · doi:10.1016/j.irbm.2014.10.002 · Cited by 11
Isolating Discriminant Neural Activity in the Presence of Eye Movements and Concurrent Task Demands
Touryan, J., Lawhern, V. J., Connolly, P. M., Bigdely-Shamlo, N., & Ries, A. J. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00357 · Cited by 11
Brain–Computer Interfaces: Neurophysiological Bases and Clinical Applications
Frolov, A., & Бобров, П. Д. (2018) · Neuroscience and Behavioral Physiology · doi:10.1007/s11055-018-0666-5 · Cited by 11
Sensorimotor contributions to working memory differ between the discrimination of Same and Different syllable pairs
Jenson, D. E., & Saltuklaroglu, T. (2021) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2021.107947 · Cited by 12
Uncovering the Interplay of Oscillatory Processes During Creative Problem Solving: A Dynamic Modeling Approach
Yu, Y., Oh, Y., Kounios, J., & Beeman, M. J. (2023) · Creativity Research Journal · doi:10.1080/10400419.2023.2172871 · Cited by 10
The contribution of executive control dysfunction to freezing of gait in Parkinson’s disease
Bayot, M., Dujardin, K., Gérard, M., Braquet, A., Tard, C., Betrouni, N., Defebvre, L., & Delval, A. (2023) · Clinical Neurophysiology · doi:10.1016/j.clinph.2023.05.010 · Cited by 10
Increased theta-low gamma phase-amplitude coupling in resting electroencephalography after intermittent theta burst stimulation
Zhang, J., Ying, C., Qian, Z., Jiao, X., Tang, X., Kong, G., Sun, J., Wang, J., & Tang, Y. (2023) · Neurophysiologie Clinique · doi:10.1016/j.neucli.2023.102899 · Cited by 10
Image memorability is linked to facilitated perceptual and semantic processing
Deng, W., Beck, D. M., & Federmeier, K. D. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00281 · Cited by 13
Excellent test-retest reliability of perturbation-evoked cortical responses supports feasibility of the balance N1 as a clinical biomarker
Mirdamadi, J. L., Poorman, A., Munter, G., Jones, K., Ting, L. H., Borich, M. R., & Payne, A. M. (2025) · Journal of Neurophysiology · doi:10.1152/jn.00583.2024 · Cited by 10
Emotional content overrides spatial attention
Bekhtereva, V., Craddock, M., & Müller, M. M. (2021) · Psychophysiology · doi:10.1111/psyp.13847 · Cited by 10
EEG–EMG coupling as a hybrid method for steering detection in car driving settings
Vecchiato, G., Vecchio, M. D., Ambeck-Madsen, J., Ascari, L., & Avanzini, P. (2022) · Cognitive Neurodynamics · doi:10.1007/s11571-021-09776-w · Cited by 14
Changes in brain activity of trainees during laparoscopic surgical virtual training assessed with electroencephalography
Suárez-Revelo, J. X., Gramann, K., Ochoa, J. F., Toro, J. P., Mejía, A. M., & Hernández, A. M. (2022) · Brain Research · doi:10.1016/j.brainres.2022.147836 · Cited by 10
Neural Correlates of Technological Ambivalence: A Research Proposal
Bala, H., Labonté-LeMoyne, É., & Léger, P. (2016) · Lecture notes in information systems and organisation · doi:10.1007/978-3-319-41402-7_11 · Cited by 10
Investigating the neural activity of various 3D visual fatigue degrees using depth‐related visual evoked potentials
Yue, K., & Wang, D. (2020) · Journal of the Society for Information Display · doi:10.1002/jsid.977 · Cited by 10
Estimating the cognitive load in physical spatial navigation
Do, T., Singh, A. K., Cortes, C., & Lin, C. (2020) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci47803.2020.9308389 · Cited by 11
High‐power gamma‐related delta phase alteration in schizophrenia patients at rest
Koshiyama, D., Miyakoshi, M., Tanaka‐Koshiyama, K., Sprock, J., & Light, G. A. (2022) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.13331 · Cited by 10
Caveats with Stochastic Gradient and Maximum Likelihood Based ICA for EEG
Montoya-Martínez, J., Cardoso, J.-F., & Gramfort, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-53547-0_27 · Cited by 10
Dynamic Responses in Brain Networks to Social Feedback: A Dual EEG Acquisition Study in Adolescent Couples
Kuo, C., Ha, T., Ebbert, A. M., Tucker, D. M., & Dishion, T. J. (2017) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2017.00046 · Cited by 11
Adjustment of Synchronization Stability of Dynamic Brain-Networks Based on Feature Fusion
Li, H., Yao, R., Xia, X., Yin, G., Deng, H., & Yang, P. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00098 · Cited by 10
Privacy for Personal NeuroinformaticsPreprint
Stopczynski, A., Greenwood, D., Hansen, L. K., & Pentland, A. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2427564 · Cited by 10
A visual working memory dataset collection with bootstrap Independent Component Analysis for comparison of electroencephalographic preprocessing pipelines
Artoni, F., Arnaud Delorme, A., & Makeig, S. (2018) · Data in Brief · doi:10.1016/j.dib.2018.12.022 · Cited by 10
Self-citation
Infraslow closed-loop brain training for anxiety and depression (ISAD): a protocol for a randomized, double-blind, sham-controlled pilot trial in adult females with internalizing disorders
Perez, T. M., Glue, P., Adhia, D. B., Navid, M. S., Zeng, J., Dillingham, P., Smith, M., Niazi, I. K., Young, C. K., & De Ridder, D. (2022) · Trials · doi:10.1186/s13063-022-06863-z · Cited by 8
“AMICA was selected based on its superior performance when compared with other ICA algorithms [237].”
Method
Valence, Arousal, and Gender Effect on Olfactory Cortical Network Connectivity: A Study Using Dynamic Causal Modeling for EEG
Rho, G., Callara, A. L., Cecchetto, C., Vanello, N., Scilingo, E. P., & Greco, A. (2022) · IEEE Access · doi:10.1109/access.2022.3226207 · Cited by 10
Auditory N1 Suppression and Omission N1 Do Not Share a Common Underlying Mechanism
Tast, V., Schröger, E., & Widmann, A. (2025) · Psychophysiology · doi:10.1111/psyp.70094 · Cited by 9
Exploring neurophysiological markers of visual perspective taking: Methodological considerations
Sæther, L. S., Roelfs, D., Moberget, T., Andreassen, O. A., Elvsåshagen, T., Jönsson, E. G., & Vaskinn, A. (2020) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2020.12.006 · Cited by 8
Reconstruction and localization of auditory sources from intracerebral SEEG using independent component analysis
López‐Madrona, V. J., Villalon, S. M., Velmurugan, J., Semeux-Bernier, A., Garnier, E. M., Badier, J., Schön, D., & Bénar, C. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119905 · Cited by 9
Predictability and decomposability separately contribute to compositional processing of idiomatic language
Hubbard, R. J., Bulkes, N. Z., & Lai, V. T. (2023) · Psychophysiology · doi:10.1111/psyp.14269 · Cited by 9
Effortful Listening Produces Both Enhancement and Suppression of Alpha in the EEG
Wisniewski, M. G., & Zakrzewski, A. C. (2023) · Auditory Perception & Cognition · doi:10.1080/25742442.2023.2218239 · Cited by 9
Identifying and Removing Interference and Artifacts in Multifractal Signals With Application to EEG Signals
Hbibi, B., Khiari, C., Wirsing, K., Mili, L., Baccar, K., & Mami, A. (2023) · IEEE Access · doi:10.1109/access.2023.3325786 · Cited by 9
EEG connectivity analysis in infants: A Beginner’s Guide on Preprocessing and Processing Techniques
Tsolisou, D. (2023) · Brain Science Advances · doi:10.26599/bsa.2023.9050025 · Cited by 10
Oscillatory power and functional connectivity in the speech change detection network
MacLean, S. E., & Ward, L. M. (2016) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2016.06.039 · Cited by 9
Resting cranial and upper cervical muscle activity is increased in patients with migraine
Janani, A. S., Pope, K. J., Fenton, N., Grummett, T. S., Bakhshayesh, H., Lewis, T. W., Watson, D. H., Whitham, E. M., & Willoughby, J. O. (2018) · Clinical Neurophysiology · doi:10.1016/j.clinph.2018.06.017 · Cited by 9
Reduced Dorsal Visual Oscillatory Activity During Working Memory Maintenance in the First-Episode Schizophrenia Spectrum
Coffman, B. A., Haas, G. L., Olson, C. R., Cho, R., Ghuman, A. S., & Salisbury, D. F. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00743 · Cited by 9
Reward and expectancy effects on neural signals of motor preparation and execution
Chen, X., van den Berg, B., & Kwak, Y. (2022) · Cortex · doi:10.1016/j.cortex.2022.01.018 · Cited by 8
Non-parametric group-level statistics for source-resolved ERP analysis
Lee, C. M., Miyakoshi, M., Arnaud Delorme, A., Cauwenberghs, G., & Makeig, S. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7320114 · Cited by 9
Self-citation
Exploiting ongoing EEG with multilinear partial least squares during free-listening to music
Wang, D., Cong, F., Zhao, Q., Toiviainen, P., Nandi, A. K., Huotilainen, M., Ristaniemi, T., & Cichocki, A. S. (2016) · Machine learning for signal processing ... · doi:10.1109/mlsp.2016.7738849 · Cited by 9
Search asymmetry in a serial auditory task: Neural source analyses of EEG implicate attention strategies
Blundon, E. G., & Ward, L. M. (2019) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.107204 · Cited by 9
Fast and effective removal of contamination from scalp electrical recordings
Janani, A. S., Grummett, T. S., Bakhshayesh, H., Lewis, T. W., DeLosAngeles, D., Whitham, E. M., Willoughby, J. O., & Pope, K. J. (2019) · Clinical Neurophysiology · doi:10.1016/j.clinph.2019.09.016 · Cited by 9
Suppression and omission effects in auditory predictive processing—Two of the same?
Tast, V., Schröger, E., & Widmann, A. (2024) · European Journal of Neuroscience · doi:10.1111/ejn.16393 · Cited by 8
Context-dependent reduction in corticomuscular coupling for balance control in chronic stroke survivors
Kukkar, K. K., Rao, N., Huynh, D., Shah, S., Contreras-Vidal, J. L., & Parikh, P. J. (2024) · Experimental Brain Research · doi:10.1007/s00221-024-06884-x · Cited by 8
An Empirical Model-Based Algorithm for Removing Motion-Caused Artifacts in Motor Imagery EEG Data for Classification Using an Optimized CNN Model
Megalingam, R. K., Sudheesh, S. K., & Manoharan, S. K. (2024) · Sensors · doi:10.3390/s24237690 · Cited by 8
Contribution of the sensorimotor beta oscillations and the cortico-basal ganglia-thalamic circuitry during value-based decision making: A simultaneous EEG-fMRI investigation
Chen, X., & Kwak, Y. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119300 · Cited by 8
An Action-Independent Role for Midfrontal Theta Activity Prior to Error Commission
Estiveira, J., Dias, C., Costa, D., Castelhano, J., Castelo-Branco, M. S., & Sousa, T. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.805080 · Cited by 8
Using Phase Shift Granger Causality to Measure Directed Connectivity in EEG Recordings
Marshall, W. J., Lackner, C. L., Marriott, P. K., Santesso, D. L., & Segalowitz, S. J. (2014) · Brain Connectivity · doi:10.1089/brain.2014.0241 · Cited by 9
High-resolution EEG source imaging of one-year-old children
Acar, Z. A., Ortiz‐Mantilla, S., BENASICH, A. A., & Makeig, S. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7590654 · Cited by 8
Self-citation
Decoding Silent Speech in Japanese from Single Trial EEGS: Preliminary Results
Yamaguchi, H., Yamazaki, T., Yamamoto, K., Ueno, S., Yamaguchi, A., Ito, T., Hirose, S., Kamijo, K., Takayanagi, H., Yamanoi, T., et al. (2015) · Journal of Computer Science & Systems Biology · doi:10.4172/jcsb.1000202 · Cited by 5
Equivalence Class Formation and the N400: Methodological Issues
Granerud, G., Arntzen, E., Elvsåshagen, T., Hatlestad‐Hall, C., & Malt, E. A. (2019) · The Psychological Record · doi:10.1007/s40732-019-00332-x · Cited by 8
EEG Effective Source Projections Are More Bilaterally Symmetric in Infants Than in Adults
Piazza, C., Cantiani, C., Miyakoshi, M., Riva, V., Molteni, M., Reni, G., & Makeig, S. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00082 · Cited by 8
Self-citation
Rhythmic neural activity is comodulated with short-term gait modifications during first-time use of a dummy prosthesis: a pilot study
Kooiman, V., van Keeken, H. G., Maurits, N. M., Weerdesteyn, V., & Solis‐Escalante, T. (2020) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-020-00761-8 · Cited by 8
Level of Orthographic Knowledge Helps to Reveal Automatic Predictions in Visual Word Processing
Huang, Z., Yang, S., Xue, L., Yang, H., Lv, Y., & Zhao, J. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.809574 · Cited by 8
Intention-based predictive information modulates auditory deviance processing
Widmann, A., & Schröger, E. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.995119 · Cited by 9
Comparison of spatial and temporal independent component analyses of electroencephalographic data: A simulation study
Knyazev, G. G. (2013) · Clinical Neurophysiology · doi:10.1016/j.clinph.2013.02.011 · Cited by 8
AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software
Weiss, S. A., Asadi‐Pooya, A. A., Vangala, S., Moy, S., Wyeth, D., Orosz, I., Gibbs, M., Schrader, L. M., Lerner, J. T., Cheng, C. K., et al. (2017) · F1000Research · doi:10.12688/f1000research.10569.2 · Cited by 8
Electrophysiological brain activity during the control of a motor imagery-based brain–computer interface
Frolov, A., Aziatskaya, G., Бобров, П. Д., Luykmanov, R. K., Федотова, И. Р., Húsek, D., & Snåšel, V. (2017) · Human Physiology · doi:10.1134/s036211971705005x · Cited by 8
Spatial Filtering Of Eeg As A Regression Problem
Spüler, M. (2020) · TUGraz OPEN Library (Graz University of Technology) · doi:10.3217/978-3-85125-533-1-84 · Cited by 8
Disentangling the origins of confidence in speeded perceptual judgments through multimodal imagingPreprint
Pereira, M., Faivre, N., Iturrate, I., Wirthlin, M., Serafini, L., Martin, S. L., Desvachez, A., Blanke, O., Ville, D. V. D., & Millán, J. D. R. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/496877 · Cited by 8
Patient specific conductivity models : characterization of the skull bones
Papageorgakis, C. (2017) · doi:10.70675/a45ba35fz1babz40dcz81aazce55ea186e47 · Cited by 8
A table tennis serve versus rally hit elicits differential hemispheric electrocortical power fluctuations
Studnicki, A., Seidler, R. D., & Ferris, D. P. (2023) · Journal of Neurophysiology · doi:10.1152/jn.00091.2023 · Cited by 7
Functional networks underlying freezing of gait: a resting-state electroencephalographic study
Bayot, M., Gérard, M., Derambure, P., Dujardin, K., Defebvre, L., Betrouni, N., & Delval, A. (2022) · Neurophysiologie Clinique · doi:10.1016/j.neucli.2022.03.003 · Cited by 7
Visual Occlusions Result in Phase Synchrony Within Multiple Brain Regions Involved in Sensory Processing and Balance Control
Symeonidou, E.-R., & Ferris, D. P. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3317055 · Cited by 7
I see artifacts: ICA-based EEG artifact removal does not improve deep network decoding across three BCI tasks
Kang, T., Chen, Y., & Wallraven, C. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad788e · Cited by 9
A feedback loop study of brain-heart interaction based on HEP and HRV
Wang, S., Wang, X., Zhao, Y., Xie, L., & Zhang, J. (2025) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2025.02.005 · Cited by 9
Linear modeling of brain activity during selective attention to continuous speech: the critical role of the N1 effect in event-related potentials to acoustic edges
Mai, A., Hillyard, S. A., & Strauß, D. J. (2025) · Cognitive Neurodynamics · doi:10.1007/s11571-025-10289-z · Cited by 7
How to Test the Quality of Reconstructed Sources in Independent Component Analysis (ICA) of EEG/MEG Data
Grosse‐Wentrup, M., Stefan Harmeling, B., Zander, T. O., Hill, J., & Schölkopf, B. (2013) · International Workshop on Pattern Recognition in NeuroImaging · doi:10.1109/prni.2013.35 · Cited by 7
MEG Based Functional Connectivity: Application of ICA to Alleviate Signal Leakage
Gohel, B., Lee, P., Kim, M., Kim, K., & Jeong, Y. (2017) · IRBM · doi:10.1016/j.irbm.2017.03.002 · Cited by 7
Facial muscle activity contaminates EEG signal at rest: evidence from frontalis and temporalis motor units
Yılmaz, G., Budan, A. S., Ungan, P., Topkara, B., & Türker, K. S. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab3235 · Cited by 7
Alpha and Beta EEG Desynchronizations Anticipate Steering Actions in a Driving Simulation Experiment
Vecchiato, G., Vecchio, M. D., Antopolskiy, S., Bellotti, A., Colucciello, A., Marchenkova, A., Ambeck-Madsen, J., Ascari, L., & Avanzini, P. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-39512-4_41 · Cited by 7
Application of Referencing Techniques in EEG-Based Recordings of Contact Heat Evoked Potentials (CHEPS)
Anders, M., Anders, B., Kreuzer, M., Zinn, S., & Walter, C. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.559969 · Cited by 7
Embodiment of sleep‐related words: Evidence from event‐related potentials
Hülsemann, M., & Rasch, B. (2021) · Psychophysiology · doi:10.1111/psyp.13824 · Cited by 7
Extraction of mismatch negativity using a resampling-based spatial filtering method
Lin, Y., Wu, W., Wu, C., Liu, B., & Gao, X. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/2/026015 · Cited by 7
Sparse inference using approximate message passing
Andersen, M. R. (2014) · Cited by 7
Resting state network activation and functional connectivity in the dying brain
Blundon, E. G., Gallagher, R. E., & Ward, L. M. (2021) · Clinical Neurophysiology · doi:10.1016/j.clinph.2021.10.018 · Cited by 7
Automatic electroencephalogram channel selection of independent component analysis based motor imagery brain-computer interface
Ruan, J., Wu, X., Zhou, B., & Guo, X. (2017) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei.2017.8302213 · Cited by 7

Authors: Delorme, A., Palmer, J., Onton, J., Oostenveld, R., & Makeig, S.
Affiliations: University of California San Diego (US)
Radboud University Nijmegen (NL)
ORCID: Palmer, Jason
Onton, Julie
Oostenveld, Robert
Makeig, Scott
Venue: PLoS ONE
Publisher: Public Library of Science (PLoS)
Published: 2012-02-15
Volume: 7
Issue: 2
Pages: e30135
ISSN: 1932-6203 (ISSN-L)
DOI: 10.1371/journal.pone.0030135
PMC: PMC3280242
OpenAlex: W2171746332
Cited by: 810
References: 57
Citations by year: 2026: 34 · 2025: 47 · 2024: 50 · 2023: 58 · 2022: 80 · 2021: 76 · 2020: 80 · 2019: 67 · 2018: 68 · 2017: 56 · 2016: 50 · 2015: 48 · 2014: 48 · 2013: 32 · 2012: 15
FWCI: 58.25
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: PLoS ONE (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
eScholarship (California Digital Library) (submittedVersion, cc-by)
HAL (submittedVersion, other-oa)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
eScholarship (California Digital Library) (submittedVersion)
Funders: National Science Foundation (0613595)
National Institute of Mental Health (NS047293)
National Institute of Neurological Disorders and Stroke (R01-NS047293-01A1, R01NS047293)
Topics: Physical Sciences › Computer Science › Signal Processing › Blind Source Separation Techniques
Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Physical Sciences › Mathematics › Computational Mathematics › Tensor decomposition and applications
Keywords: independent component analysis, blind source separation, EEG source localization, dipole modeling, volume conduction, mutual information, principal component analysis, scalp EEG
MeSH terms: Algorithms, Brain, Brain Mapping, Computer Simulation, Electroencephalography, Humans, Scalp, Signal Processing, Computer-Assisted, Principal Component Analysis

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

Independent EEG Sources Are Dipolar

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks