An Information-Maximization Approach to Blind Separation and Blind Deconvolution

Neural Networks: A Comprehensive Foundation

Haykin, S. (1998) · Cited by 30,070

Probability, Random Variables, and Stochastic Processes.

Abrahams, J., & Papoulis, A. (1984) · Journal of the American Statistical Association · doi:10.2307/2288754 · Cited by 16,301

Independent component analysis, A new concept?

Comon, P. (1994) · Signal Processing · doi:10.1016/0165-1684(94)90029-9 · Cited by 8,324

A Fast Fixed-Point Algorithm for Independent Component Analysis

Hyvärinen, A., & Erkki Oja, A. (1997) · Neural Computation · doi:10.1162/neco.1997.9.7.1483 · Cited by 3,373

Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture

Jutten, C., & Hérault, J. (1991) · Signal Processing · doi:10.1016/0165-1684(91)90079-x · Cited by 2,550

Adaptive filter theory (2nd ed.)

Haykin, S. (1991) · Prentice-Hall, Inc eBooks · Cited by 1,905

Unsupervised Learning

Barlow, H. B. (1989) · The MIT Press eBooks · doi:10.1162/neco.1989.1.3.295 · Cited by 1,709

First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method

Battiti, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.2.141 · Cited by 1,193

What Is the Goal of Sensory Coding?

Field, D. J. (1994) · Neural Computation · doi:10.1162/neco.1994.6.4.559 · Cited by 1,183

A Simple Coding Procedure Enhances a Neuron's Information Capacity

Laughlin, S. B. (1981) · Zeitschrift für Naturforschung C · doi:10.1515/znc-1981-9-1040 · Cited by 1,015

Natural Gradient Learning for Over- and Under-Complete Bases in ICA

Амари, Ш.-И. (1999) · Neural Computation · doi:10.1162/089976699300015990 · Cited by 172

Nonnegative Matrix Factorization for Rapid Recovery of Constituent Spectra in Magnetic Resonance Chemical Shift Imaging of the Brain

Sajda, P., Du, S., Brown, T. R., Stoyanova, R. S., Shungu, D. C., Mao, X., & Parra, L. C. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.834626 · Cited by 171

Combining reconstructive and discriminative subspace methods for robust classification and regression by subsampling

Fidler, S., Skočaj, D., & Leonardis, A. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.46 · Cited by 170

Fast ICA for noisy data using Gaussian moments

Hyvärinen, A. (2003) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.1999.777510 · Cited by 170

Sparse image coding using a 3D non-negative tensor factorization

Hazan, T., Polak, S., & Shashua, A. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.228 · Cited by 173

The Phase of Thalamic Alpha Activity Modulates Cortical Gamma-Band Activity: Evidence from Resting-State MEG Recordings

Roux, F., Wibral, M., Singer, W., Aru, J., & Uhlhaas, P. J. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.5778-12.2013 · Cited by 169

Comparison of three methods for generating group statistical inferences from independent component analysis of functional magnetic resonance imaging data

Schmithorst, V. J., & Holland, S. K. (2004) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.20009 · Cited by 171

Audio source separation with a single sensor

Benaroya, L., Bimbot, F., & Gribonval, R. (2005) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tsa.2005.854110 · Cited by 169

Association between abnormal default mode network activity and suicidality in depressed adolescents

Zhang, S., Chen, J.-M., Kuang, L., Cao, J., Zhang, H., Ai, M., Wang, W., Zhang, S.-D., Wang, S.-Y., Liu, S.-J., et al. (2016) · BMC Psychiatry · doi:10.1186/s12888-016-1047-7 · Cited by 163

Informing cognitive abstractions through neuroimaging: The neural drift diffusion model.

Turner, B. M., van Maanen, L., & Forstmann, B. U. (2015) · Psychological Review · doi:10.1037/a0038894 · Cited by 182

Addressing Misallocation of Variance in Principal Components Analysis of Event-Related Potentials

Dien, J. (1998) · Brain Topography · doi:10.1023/a:1022218503558 · Cited by 165

A Rapid Form of Offline Consolidation in Skill Learning

Bönstrup, M., Iturrate, I., Thompson, R., Cruciani, G., Censor, N., & Cohen, L. G. (2019) · Current Biology · doi:10.1016/j.cub.2019.02.049 · Cited by 180

Flexible Independent Component Analysis

Choi, S., Cichocki, A. S., & Амари, Ш.-И. (2000) · The Journal of VLSI Signal Processing Systems for Signal Image and Video Technology · doi:10.1023/a:1008135131269 · Cited by 166

It's how you get there: walking down a virtual alley activates premotor and parietal areas

Wagner, J., Solis‐Escalante, T., Scherer, R., Neuper, C., & Müller-Putz, G. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00093 · Cited by 171

Spatiotemporal Blind Source Separation Approach to Atrial Activity Estimation in Atrial Tachyarrhythmias

Castells, F., Rieta, J. J., Millet, J., & Zarzoso, V. (2005) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.840473 · Cited by 163

Mean-Field Approaches to Independent Component Analysis

Højen-Sørensen, P. A. D. F. R., Winther, O., & Hansen, L. K. (2002) · Neural Computation · doi:10.1162/089976602317319009 · Cited by 165

Ensemble Learning for Blind Image Separation and Deconvolution

Miskin, J. W., & Mackay, D. (2000) · Perspectives in neural computing · doi:10.1007/978-1-4471-0443-8_7 · Cited by 164

Feature extraction for classification problems and its application to face recognition

Kwak, N. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.10.012 · Cited by 164

Evolving Signal Processing for Brain–Computer Interfaces

Makeig, S., Kothe, C. A., Mullen, T. R., Bigdely-Shamlo, N., Zhang, Z., & Kreutz-Delgado, K. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2185009 · Cited by 164

Muscle synergies in neuroscience and robotics: from input-space to task-space perspectives

Alessandro, C., Delis, I., Nori, F., Panzeri, S., & Berret, B. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00043 · Cited by 159

Perceptual image quality assessment using a normalized Laplacian pyramid

Laparra, V., Ballé, J., Berardino, A., & Simoncelli, E. P. (2016) · Electronic Imaging · doi:10.2352/issn.2470-1173.2016.16.hvei-103 · Cited by 177

Methods for analysis of brain connectivity: An IFCN-sponsored review

Rossini, P. M., Iorio, R. D., Bentivoglio, M., Bertini, G., Ferreri, F., Gerloff, C. P., Ilmoniemi, R. J., Miraglia, F., Nitsche, M. A., Pestilli, F., et al. (2019) · Clinical Neurophysiology · doi:10.1016/j.clinph.2019.06.006 · Cited by 169

Nonlinear blind source separation using a radial basis function network

Tan, Y., Wang, J., & Zurada, J. M. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.896801 · Cited by 163

Cerebral peak alpha frequency predicts individual differences in pain sensitivity

Furman, A. J., Meeker, T. J., Rietschel, J. C., Yoo, S., Muthulingam, J. A., Prokhorenko, M., Keaser, M. L., Goodman, R. N., Mazaheri, A., & Seminowicz, D. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.042 · Cited by 162

Techniques for Chronic, Multisite Neuronal Ensemble Recordings in Behaving Animals

Kralik, J. D., Dimitrov, D. F., Krupa, D. J., Katz, D. B., Cohen, D., & Nicolelis, M. A. L. (2001) · Methods · doi:10.1006/meth.2001.1231 · Cited by 162

Artifact Processing in Computerized Analysis of Sleep EEG – A Review

Anderer, P., Roberts, S., Schlögl, A., Gruber, G., Klösch, G., Herrmann, W. M., Rappelsberger, P., Filz, O., Barbanoj, M. J., Dorffner, G., et al. (1999) · Neuropsychobiology · doi:10.1159/000026613 · Cited by 162

Information Bottleneck for Gaussian Variables

Chechik, G., Globerson, A., Tishby, N., & Weiss, Y. (2003) · Cited by 161

Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity

Zander, T. O., Krol, L. R., Birbaumer, N., & Gramann, K. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1605155114 · Cited by 161

Independent component analysis in the presence of Gaussian noise by maximizing joint likelihood

Hyvärinen, A. (1998) · Neurocomputing · doi:10.1016/s0925-2312(98)00049-6 · Cited by 161

Noninvasive Neural Prostheses Using Mobile and Wireless EEG

Lin, C., Ko, L., Chiou, J., Duann, J., Huang, R., Liang, S., Chiu, T.-W., & Jung, T. (2008) · Proceedings of the IEEE · doi:10.1109/jproc.2008.922561 · Cited by 160

Dynamic reorganization of brain functional networks during cognition

Bola, M., & Sabel, B. A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.03.057 · Cited by 162

Kernel Nonparametric Weighted Feature Extraction for Hyperspectral Image Classification

Kuo, B., Li, C., & Yang, J. (2009) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2008.2008308 · Cited by 160

Semi-blind ICA of fMRI: a method for utilizing hypothesis-derived time courses in a spatial ICA analysis

Calhoun, V. D., Adali, T., Stevens, M. C., Kiehl, K. A., & Pekar, J. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.12.012 · Cited by 159

Quantitative change of EEG and respiration signals during mindfulness meditation

Ahani, A., Wahbeh, H., Nezamfar, H., Miller, M., Erdogmus, D., & Oken, B. (2014) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-11-87 · Cited by 159

Intrinsic Visual-Motor Synchrony Correlates With Social Deficits in Autism

Nebel, M. B., Eloyan, A., Nettles, C., Sweeney, K. L., Ament, K. A., Ward, R. E., Choe, A. S., Barber, A. D., Pekar, J. J., & Mostofsky, S. H. (2015) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.08.029 · Cited by 169

Methodological aspects of EEG and body dynamics measurements during motion

Reis, P., Hebenstreit, F., Gabsteiger, F., von Tscharner, V., & Lochmann, M. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00156 · Cited by 160

Real-time independent component analysis of fMRI time-series

Esposito, F. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.08.012 · Cited by 159

Functional Networks in the Anesthetized Rat Brain Revealed by Independent Component Analysis of Resting-State fMRI

Hutchison, R. M., Mirsattari, S. M., Jones, C. K., Gati, J. S., & Leung, L.-W. S. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00141.2010 · Cited by 165

P300 amplitude variation is related to ventral striatum BOLD response during gain and loss anticipation: An EEG and fMRI experiment

Pfabigan, D. M., Seidel, E., Sladky, R., Hahn, A., Paul, K., Grahl, A., Küblböck, M., Kraus, C., Hummer, A., Kranz, G. S., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.077 · Cited by 166

A method for multitask fMRI data fusion applied to schizophrenia

Calhoun, V. D., Adalı, T., Kiehl, K. A., Astur, R. S., Pekar, J. J., & Pearlson, G. D. (2005) · Human Brain Mapping · doi:10.1002/hbm.20204 · Cited by 162

Sparse Feature Fidelity for Perceptual Image Quality Assessment

Chang, H.-W., Yang, H., Gan, Y., & Wang, M. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2266579 · Cited by 159

Adaptive EEG-Based Alertness Estimation System by Using ICA-Based Fuzzy Neural Networks

Lin, C., Ko, L., Chung, I., Huang, T., Chen, Y.-C., Jung, T., & Liang, S. (2006) · IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications · doi:10.1109/tcsi.2006.884408 · Cited by 158

Temporal cascade of frontal, motor and muscle processes underlying human action-stopping

Jana, S., Hannah, R., Muralidharan, V., & Aron, A. R. (2020) · eLife · doi:10.7554/elife.50371 · Cited by 157

Psilocybin-induced spiritual experiences and insightfulness are associated with synchronization of neuronal oscillations

Kometer, M., Pokorny, T., Seifritz, E., & Volleinweider, F. X. (2015) · Psychopharmacology · doi:10.1007/s00213-015-4026-7 · Cited by 169

Correlation between amygdala BOLD activity and frontal EEG asymmetry during real-time fMRI neurofeedback training in patients with depression

Zotev, V. S., Yuan, H., Misaki, M., Phillips, R., Young, K. D., Feldner, M. T., & Bodurka, J. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.02.003 · Cited by 165

Isolating physiologic noise sources with independently determined spatial measures

Beall, E. B., & Lowe, M. J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.004 · Cited by 157

Analysis of Multimodal Neuroimaging Data

Bießmann, F., Plis, S., Meinecke, F. C., Eichele, T., & Müller, K. (2011) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2011.2170675 · Cited by 163

Decoding human brain activity during real-world experiences

Spiers, H. J., & Maguire, E. A. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.06.002 · Cited by 163

Neural networks expand SP's horizons

Haykin, S. (1996) · IEEE Signal Processing Magazine · doi:10.1109/79.487040 · Cited by 161

Identification of large-scale networks in the brain using fMRI

Bellec, P., Perlbarg, V., Jbabdi, S., Pélégrini‐Issac, M., Anton, J., Doyon, J., & Benali, H. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.08.044 · Cited by 154

Decoding Covert Speech From EEG-A Comprehensive Review

Panachakel, J. T., & Ramakrishnan, A. G. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.642251 · Cited by 156

Predicting Age From Brain EEG Signals—A Machine Learning Approach

Zoubi, O. A., Wong, C. K., Kuplicki, R., Yeh, H., Mayeli, A., Refai, H. H., Paulus, M. P., & Bodurka, J. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00184 · Cited by 156

Rostral cingulate zone and correct response monitoring: ICA and source localization evidences for the unicity of correct- and error-negativities

Roger, C., Bénar, C. G., Vidal, F., Hasbroucq, T., & Burle, B. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.02.005 · Cited by 153

Thalamus and posterior temporal lobe show greater inter-network connectivity at rest and across sensory paradigms in schizophrenia

Çetin, M. S., Christensen, F., Abbott, C. C., Stephen, J. M., Mayer, A. R., Cañive, J. M., Bustillo, J. R., Pearlson, G. D., & Calhoun, V. D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.04.009 · Cited by 154

Connectivity measures applied to human brain electrophysiological data

Greenblatt, R. E., Pflieger, M. E., & Ossadtchi, A. E. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.02.025 · Cited by 158

A Hierarchical Bayesian Model for Learning Nonlinear Statistical Regularities in Nonstationary Natural Signals

Karklin, Y., & Lewicki, M. S. (2005) · Neural Computation · doi:10.1162/0899766053011474 · Cited by 155

Auditory Oddball Deficits in Schizophrenia: An Independent Component Analysis of the fMRI Multisite Function BIRN Study

Kim, D. I., Mathalon, D. H., Ford, J. M., Mannell, M. V., Turner, J. A., Brown, G. G., Belger, A., Gollub, R. L., Lauriello, J., Wible, C. G., et al. (2008) · Schizophrenia Bulletin · doi:10.1093/schbul/sbn133 · Cited by 159

Approaches to revealing the neural basis of muscle synergies: a review and a critique

Cheung, V. C. K., & Seki, K. (2021) · Journal of Neurophysiology · doi:10.1152/jn.00625.2019 · Cited by 165

Divergent brain network connectivity in amyotrophic lateral sclerosis

Agosta, F., Canu, E., Valsasina, P., Riva, N., Prelle, A., Comi, G., & Filippi, M. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2012.04.015 · Cited by 155

Resting state connectivity differences in eyes open versus eyes closed conditions

Agcaoglu, O., Wilson, T. W., Wang, Y., Stephen, J. M., & Calhoun, V. D. (2019) · Human Brain Mapping · doi:10.1002/hbm.24539 · Cited by 158

The frequency gradient of human resting-state brain oscillations follows cortical hierarchies

Mahjoory, K., Schoffelen, J., Keitel, A., & Groß, J. (2020) · eLife · doi:10.7554/elife.53715 · Cited by 163

AUTOMATIC ARTIFACT REJECTION FOR EEG DATA USING HIGH-ORDER STATISTICS AND INDEPENDENT COMPONENT ANALYSIS

Arnaud Delorme, A., Makeig, S., B, T. J. S. A., & Pines, N. T. (2001) · Cited by 154

Sequential Activation of Muscle Synergies During Locomotion in the Intact Cat as Revealed by Cluster Analysis and Direct Decomposition

Krouchev, N. I., Kalaska, J. F., & Drew, T. (2006) · Journal of Neurophysiology · doi:10.1152/jn.00241.2006 · Cited by 152

Dissociable medial frontal negativities from a common monitoring system for self- and externally caused failure of goal achievement

Gentsch, A., Ullsperger, P., & Ullsperger, M. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.064 · Cited by 168

Encoding and Decoding Models in Cognitive Electrophysiology

Holdgraf, C., Rieger, J. W., Micheli, C., Martin, S., Knight, R. T., & Theunissen, F. E. (2017) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2017.00061 · Cited by 154

Information–theoretic approach to blind separation of sources in non-linear mixture

Yang, H. H., Амари, Ш.-И., & Cichocki, A. S. (1998) · Signal Processing · doi:10.1016/s0165-1684(97)00196-5 · Cited by 153

Restricted Boltzmann machines for neuroimaging: An application in identifying intrinsic networks

Hjelm, R. D., Calhoun, V. D., Salakhutdinov, R., Allen, E. A., Adalı, T., & Plis, S. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.048 · Cited by 154

Complexity Pursuit: Separating Interesting Components from Time Series

Hyvärinen, A. (2001) · Neural Computation · doi:10.1162/089976601300014394 · Cited by 152

Blind source separation of real world signals

Lee, T.-W., Bell, A. J., & Orglmeister, R. (2002) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/icnn.1997.614235 · Cited by 152

Event-related brain response abnormalities in autism: evidence for impaired cerebello-frontal spatial attention networks

Townsend, J., Westerfield, M., Leaver, E., Makeig, S., Jung, T.-P., Pierce, K., & Courchesne, E. (2001) · Cognitive Brain Research · doi:10.1016/s0926-6410(00)00072-0 · Cited by 151

An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data

Schirner, M., Rothmeier, S., Jirsa, V., McIntosh, A. R., & Ritter, P. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.03.055 · Cited by 147

Examining ERP correlates of recognition memory: Evidence of accurate source recognition without recollection

Addante, R. J., Ranganath, C., & Yonelinas, A. P. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.04.031 · Cited by 148

The EEG signature of sensory evidence accumulation during decision formation closely tracks subjective perceptual experience

Tagliabue, C. F., Veniero, D., Benwell, C., Cecere, R., Savazzi, S., & Thut, G. (2019) · Scientific Reports · doi:10.1038/s41598-019-41024-4 · Cited by 150

Spectroscopic Analysis of Neural Activity in Brain: Increased Oxygen Consumption Following Activation of Barrel Cortex

Mayhew, J. E., Johnston, D. K., Berwick, J., Jones, M., Coffey, P. J., & Zheng, Y. (2000) · NeuroImage · doi:10.1006/nimg.2000.0656 · Cited by 150

EEG characteristics in “eyes-open” versus “eyes-closed” conditions: Small-world network architecture in healthy aging and age-related brain degeneration

Miraglia, F., Vecchio, F., Bramanti, P., & Rossini, P. M. (2016) · Clinical Neurophysiology · doi:10.1016/j.clinph.2015.07.040 · Cited by 150

Independent vector analysis (IVA): Multivariate approach for fMRI group study

Lee, J., Lee, T.-W., Jolesz, F. A., & Yoo, S. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.11.019 · Cited by 151

Interventional Video Grounding with Dual Contrastive Learning

Nan, G., Qiao, R., Xiao, Y., Liu, J., Leng, S., Zhang, H., & Lu, W. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00279 · Cited by 154

Ensemble learning for independent component analysis

Cheng, J., Liu, Q., Lu, H., & Chen, Y. (2005) · Pattern Recognition · doi:10.1016/j.patcog.2005.06.018 · Cited by 149

Blind identification of damage in time-varying systems using independent component analysis with wavelet transform

Yang, Y., & Nagarajaiah, S. (2012) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2012.08.029 · Cited by 147

Altered Topological Properties of Functional Network Connectivity in Schizophrenia during Resting State: A Small-World Brain Network Study

Yu, Q., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Rachakonda, S., He, H., Gruner, W. E., Pearlson, G. D., Kiehl, K. A., & Calhoun, V. D. (2011) · PLoS ONE · doi:10.1371/journal.pone.0025423 · Cited by 148

Evolution-Based Functional Decomposition of Proteins

Rivoire, O., Reynolds, K. A., & Ranganathan, R. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004817 · Cited by 152

Human female attractiveness: waveform analysis of body shape

Tovée, M. J., Hancock, P., Mahmoodi, S., Singleton, B. R. R., & Cornelissen, P. L. (2002) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2002.2133 · Cited by 150

A Context-Sensitive Generalization of ICA

Pearlmutter, B. A., & Parra, L. C. (1996) · Maynooth University ePrints and eTheses Archive (Maynooth University) · Cited by 148

Cellular Level Brain Imaging in Behaving Mammals: An Engineering Approach

Hamel, E. O., Grewe, B. F., Parker, J. G., & Schnitzer, M. J. (2015) · Neuron · doi:10.1016/j.neuron.2015.03.055 · Cited by 150

Independent Component Analysis by Entropy Bound Minimization

Li, X.-L., & Adalı, T. (2010) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2010.2055859 · Cited by 147

Memantine Effects on Electroencephalographic Measures of Putative Excitatory/Inhibitory Balance in Schizophrenia

Molina, J. L., Voytek, B., Thomas, M. L., Joshi, Y. B., Bhakta, S. G., Talledo, J., Swerdlow, N. R., & Light, G. A. (2020) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2020.02.004 · Cited by 153

Temporal dynamics of access to consciousness in the attentional blink

Kranczioch, C., Debener, S., Maye, A., & Engel, A. K. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.05.044 · Cited by 143

Task-related component analysis for functional neuroimaging and application to near-infrared spectroscopy data

Tanaka, H., Katura, T., & Sato, H. R. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.044 · Cited by 150

INDEPENDENT COMPONENT ANALYSIS OF BIOMEDICAL SIGNALS

Jung, T., Makeig, S., Lee, T.-W., McKeown, M. J., Brown, G. D., Bell, A. J., & B, T. J. S. A. (2000) · Cited by 146

Elements of Computational Statistics

Gentle, J. E. (2002) · Springer eBooks · doi:10.1007/b97337 · Cited by 146

Independent component analysis of fMRI data in the complex domain

Calhoun, V. D., Adalı, T., Pearlson, G. D., van Zijl, P. C. M., & Pekar, J. J. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10202 · Cited by 146

Blind source separation using Renyi's mutual information

Hild, K. E., Deniz Erdogmus, B., & Príncipe, J. C. (2001) · IEEE Signal Processing Letters · doi:10.1109/97.923043 · Cited by 146

Neuronal message passing using mean-field, bethe, and marginal approximations

Parr, T., Markovic, D., Kiebel, S. J., & Friston, K. J. (2019) · Scientific Reports · doi:10.1038/s41598-018-38246-3 · Cited by 128

Wavelet analysis in neurodynamics

Pavlov, A. N., Hramov, A. E., Koronovskii, A. A., Sitnikova, E., Makarov, V. A., & Ovchinnikov, A. A. (2012) · Physics-Uspekhi · doi:10.3367/ufne.0182.201209a.0905 · Cited by 146

Intrinsic Hippocampal Activity as a Biomarker for Cognition and Symptoms in Schizophrenia

Tregellas, J. R., Smucny, J., Harris, J. G., D, A. O. M., Maharajh, K., Kronberg, E., Eichman, L. C., Lyons, E. R., & Freedman, R. (2014) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2013.13070981 · Cited by 148

Brain–Computer Interfaces

He, B., Yuan, H., Meng, J., & Gao, S. (2020) · Springer eBooks · doi:10.1007/978-3-030-43395-6_4 · Cited by 150

Single-trial classification of EEG in a visual object task using ICA and machine learning

Stewart, A. X., Nuthmann, A., & Sanguinetti, G. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2014.02.014 · Cited by 148

EEG Alpha Power Is Modulated by Attentional Changes during Cognitive Tasks and Virtual Reality Immersion

Magosso, E., Crescenzio, F. D., Ricci, G., Piastra, S., & Ursino, M. (2019) · Computational Intelligence and Neuroscience · doi:10.1155/2019/7051079 · Cited by 161

An Unsupervised Sentence Embedding Method by Mutual Information Maximization

Zhang, Y., He, R., Liu, Z., Lim, K. H., & Bing, L. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.124 · Cited by 174

Evolving schema representations in orbitofrontal ensembles during learning

Zhou, J., Jia, C., Montesinos-Cartagena, M., Gardner, M., Zong, W., & Schoenbaum, G. M. (2020) · Nature · doi:10.1038/s41586-020-03061-2 · Cited by 148

ARTICLE Coni~nunicated by John Platt and Simon Haykin An Information-Maximization Approach to Blind Separation and Blind Deconvolution

Bell, A. J., & B, T. J. S. A. (2008)

An Information-Maximisation Approach to Blind Separation and Blind Deconvolution

Bell, A. J., & B, T. J. S. A. (1995)

A Non-linear Information Maximisation Algorithm that Performs Blind Separation

Bell, A. J., & Sejnowski, T. J. (1994) · Cited by 31

Blind separation of convolved sources based on information maximization

Torkkola, K. (2002) · Machine learning for signal processing ... · doi:10.1109/nnsp.1996.548372 · Cited by 237

Blind separation and blind deconvolution: an information-theoretic approach

Bell, A. J., & B, T. J. S. A. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1995.479719 · Cited by 48