The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Abstract
We derive a new self-organizing learning algorithm that maximizes the information transferred in a network of nonlinear units. The algorithm does not assume any knowledge of the input distributions, and is defined here for the zero-noise limit. Under these conditions, information maximization has extra properties not found in the linear case (Linsker 1989). The nonlinearities in the transfer function are able to pick up higher-order moments of the input distributions and perform something akin to true redundancy reduction between units in the output representation. This enables the network to separate statistically independent components in the inputs: a higher-order generalization of principal components analysis. We apply the network to the source separation (or cocktail party) problem, successfully separating unknown mixtures of up to 10 speakers. We also show that a variant on the network architecture is able to perform blind deconvolution (cancellation of unknown echoes and reverberation in a speech signal). Finally, we derive dependencies of information transfer on time delays. We suggest that information maximization provides a unifying framework for problems in "blind" signal processing.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Subsequently, extended infomax independent components analysis (ICA)63,64 was applied to detect eye movement-related artifacts.”
From Event-related potentials of automatic imitation are modulated by ethnicity during stimulus processing, but not during motor execution (2018) · cites this paper as [63]
In the citing paper
“Independent component analysis (ICA) decomposes the observed response into a linear mixture of non-Gaussian causes [36].”
From Hierarchical Models in the Brain (2008) · cites this paper as [36]
In the citing paper
“Next, we ran the Infomax ICA algorithm [Bell and Sejnowski, 1995], repeating it 15 times in ICASSO, implemented in GIFT, to generate a stable set of 75 final components.”
From Specific default mode subnetworks support mentalizing as revealed through opposing network recruitment by social and semantic FMRI tasks: Mentalizing and Default-Mode Subnetworks (2015) · cites this paper as Bell and Sejnowski, 1995
Citations
Cites
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
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
Flagged first, then by citations
Research works printed but not linked: 1
- Li S., 1994, I E E E J. Oceanic Eng., 20, 73
Cited by
Sorted by citations
Items 401–500 of 1,038
Evaluation of PCA and ICA of simulated ERPs: Promax vs. infomax rotations
Dien, J., Khoe, W., & Mangun, G. R. (2006) · Human Brain Mapping · doi:10.1002/hbm.20304 · Cited by 200
Connectome: Graph theory application in functional brain network architecture
Vecchio, F., Miraglia, F., & Rossini, P. M. (2017) · Clinical Neurophysiology Practice · doi:10.1016/j.cnp.2017.09.003 · Cited by 208
Prestimulus EEG Power Predicts Conscious Awareness But Not Objective Visual Performance
Benwell, C., Tagliabue, C. F., Veniero, D., Cecere, R., Savazzi, S., & Thut, G. (2017) · eNeuro · doi:10.1523/eneuro.0182-17.2017 · Cited by 219
Simultaneous encoding of tactile information by three primate cortical areas
Nicolelis, M. A. L., Ghazanfar, A. A., Stambaugh, C. R., Oliveira, L. M. O., Laubach, M., Chapin, J. K., Nelson, R. J., & Kaas, J. H. (1998) · Nature Neuroscience · doi:10.1038/2855 · Cited by 202
The Functional and Structural Neural Basis of Individual Differences in Loss Aversion
Canessa, N., Crespi, C., Motterlini, M., Baud‐Bovy, G., Chierchia, G., Pantaleo, G., Tettamanti, M., & Cappa, S. F. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.0497-13.2013 · Cited by 217
Debener, S., Strobel, A., Sorger, B., Peters, J. C., Kranczioch, C., Engel, A. K., & Goebel, R. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.09.031 · Cited by 201
CORSICA: correction of structured noise in fMRI by automatic identification of ICA components
Perlbarg, V., Bellec, P., Anton, J., Pélégrini‐Issac, M., Doyon, J., & Benali, H. (2006) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2006.09.042 · Cited by 201
Petersen, N., Kilpatrick, L. A., Goharzad, A., & Cahill, L. F. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.12.016 · Cited by 205
Face recognition using independent component analysis and support vector machines
Déniz, Ó., Castrillón-Santana, M., & Hernández, M. (2003) · Pattern Recognition Letters · doi:10.1016/s0167-8655(03)00081-3 · Cited by 200
Data-driven clustering reveals a fundamental subdivision of the human cortex into two global systems
Golland, Y., Golland, P., Bentin, S., & Malach, R. (2008) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2007.10.003 · Cited by 190
Nikulin, V. V., Nolte, G., & Curio, G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.057 · Cited by 203
A family of fixed-point algorithms for independent component analysis
Hyvärinen, A. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1997.604766 · Cited by 199
Developments of the generative topographic mapping
Bishop, C., Svensén, M., & Williams, C. K. I. (1998) · Neurocomputing · doi:10.1016/s0925-2312(98)00043-5 · Cited by 199
Grouiller, F., Thornton, R. C., Groening, K., Spinelli, L., Duncan, J. S., Schaller, K., Siniatchkin, M., Lemieux, L., Seeck, M., Michel, C. M., et al. (2011) · Brain · doi:10.1093/brain/awr156 · Cited by 199
A survey of hybrid ANN/HMM models for automatic speech recognition
Trentin, E., & Gori, M. (2001) · Neurocomputing · doi:10.1016/s0925-2312(00)00308-8 · Cited by 198
Artoni, F., Arnaud Delorme, A., & Makeig, S. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.03.016 · Cited by 198
Monaural speech separation and recognition challenge
Cooke, M. P., Hershey, J. R., & Rennie, S. J. (2009) · Computer Speech & Language · doi:10.1016/j.csl.2009.02.006 · Cited by 197
Face representation using independent component analysis
Yuen, P. C., & Lai, J. (2002) · Pattern Recognition · doi:10.1016/s0031-3203(01)00101-7 · Cited by 197
What and where: A Bayesian inference theory of attention
Chikkerur, S. S., Serre, T., Tan, C., & Poggio, T. (2010) · Vision Research · doi:10.1016/j.visres.2010.05.013 · Cited by 202
Comparing test-retest reliability of dynamic functional connectivity methods
Choe, A. S., Nebel, M. B., Barber, A. D., Cohen, J. R., Xu, Y., Pekar, J. J., Caffo, B. S., & Lindquist, M. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.005 · Cited by 197
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
Least-dependent-component analysis based on mutual information
Stögbauer, H., Kraskov, A., Astakhov, S. A., & Grassberger, P. (2004) · Physical Review E · doi:10.1103/physreve.70.066123 · Cited by 202
An Introduction to Independent Component Analysis: InfoMax and FastICA algorithms
Langlois, D., Chartier, S., & Gosselin, D. (2010) · Tutorials in Quantitative Methods for Psychology · doi:10.20982/tqmp.06.1.p031 · Cited by 196
A Data-Driven Sparse GLM for fMRI Analysis Using Sparse Dictionary Learning With MDL Criterion
Lee, K., Tak, S., & Ye, J. C. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2097275 · Cited by 194
Distinct β Band Oscillatory Networks Subserving Motor and Cognitive Control during Gait Adaptation
Wagner, J., Makeig, S., Gola, M. K., Neuper, C., & Müller-Putz, G. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.3543-15.2016 · Cited by 213
Umeyama, S., & Godin, G. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1273960 · Cited by 195
Neuronal chronometry of target detection: Fusion of hemodynamic and event-related potential data
Calhoun, V. D., Adalı, T., Pearlson, G. D., & Kiehl, K. A. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.08.060 · Cited by 196
A Novel Representation Learning for Dynamic Graphs Based on Graph Convolutional Networks
Gao, C., Zhu, J., Zhang, F., Wang, Z., & Li, X. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3159661 · Cited by 198
Linear image coding for regression and classification using the tensor-rank principle
Shashua, A., & Levin, A. (2005) · 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/cvpr.2001.990454 · Cited by 196
Yang, H., Liu, J., Jing Sui (Beijing Normal University), my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Pearlson, G. D., & Calhoun, V. D. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00192 · Cited by 193
Functional principal component analysis of fMRI data
Viviani, R., Grön, G., & Spitzer, M. (2004) · Human Brain Mapping · doi:10.1002/hbm.20074 · Cited by 191
Arfanakis, K., Cordes, D., Haughton, V. M., Moritz, C. H., Quigley, M. A., & Meyerand, M. E. (2000) · Magnetic Resonance Imaging · doi:10.1016/s0730-725x(00)00190-9 · Cited by 191
Bell, A. J. (2003) · Cited by 191
Self-citationCalhoun, V. D., Pekar, J. J., & Pearlson, G. D. (2004) · Neuropsychopharmacology · doi:10.1038/sj.npp.1300543 · Cited by 192
Cortical reorganization in children with cochlear implants
Gilley, P. M., Sharma, A., & Dorman, M. F. (2008) · Brain Research · doi:10.1016/j.brainres.2008.08.026 · Cited by 191
Removing electroencephalographic artifacts: comparison between ICA and PCA
Jung, T., Humphries, C., Lee, T.-W., Makeig, S., McKeown, M. J., Iragui, V. J., & B, T. J. S. A. (2002) · Machine learning for signal processing ... · doi:10.1109/nnsp.1998.710633 · Cited by 191
Causal Inference by Independent Component Analysis: Theory and Applications*
Moneta, A., Entner, D., Hoyer, P. O., & Coad, A. (2012) · Oxford Bulletin of Economics and Statistics · doi:10.1111/j.1468-0084.2012.00710.x · Cited by 206
Romero, S., Mañanas, M. Á., & Barbanoj, M. J. (2008) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2007.12.001 · Cited by 191
A Variational Method for Learning Sparse and Overcomplete Representations
Girolami, M. (2001) · Neural Computation · doi:10.1162/089976601753196003 · Cited by 191
Modulation of Muscle Synergy Recruitment in Primate Grasping
Overduin, S. A., d’Avella, A., Roh, J., & Bizzi, E. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.2869-07.2008 · Cited by 190
Brain dynamics during natural viewing conditions—A new guide for mapping connectivity in vivo
Bartels, A., & Zeki, S. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.08.044 · Cited by 189
Performance of blind source separation algorithms for fMRI analysis using a group ICA method
Correa, N. M., Adalı, T., & Calhoun, V. D. (2006) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2006.10.017 · Cited by 191
Functional networks for cognitive control in a stop signal task: Independent component analysis
Zhang, S., & Li, C. R. (2011) · Human Brain Mapping · doi:10.1002/hbm.21197 · Cited by 195
Corradi‐Dell’Acqua, C., Tusche, A., Vuilleumier, P. O., & Singer, T. (2016) · Nature Communications · doi:10.1038/ncomms10904 · Cited by 199
Remote Measurements of Heart and Respiration Rates for Telemedicine
Zhao, F., Li, M., Yi, Q., & Tsien, J. Z. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071384 · Cited by 190
Akhtar, M. T., Mitsuhashi, W., & James, C. J. (2011) · Signal Processing · doi:10.1016/j.sigpro.2011.08.005 · Cited by 189
Neural Correlates of Trial-to-Trial Fluctuations in Response Caution
van Maanen, L., Brown, S. D., Eichele, T., Wagenmakers, E., Ho, T. C., Serences, J. T., & Forstmann, B. U. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.2924-11.2011 · Cited by 199
Defect Detection in Solar Modules Using ICA Basis Images
Tsai, D., Wu, S.-C., & Chiu, W. (2012) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2012.2209663 · Cited by 189
Blind Separation of Time-Delayed and Convolved Sources
Lee, T.-W. (1998) · Springer eBooks · doi:10.1007/978-1-4757-2851-4_4 · Cited by 189
Minimization of cochlear implant stimulus artifact in cortical auditory evoked potentials
Gilley, P. M., Sharma, A., Dorman, M. F., Finley, C. C., Panch, A. S., & Martin, K. (2006) · Clinical Neurophysiology · doi:10.1016/j.clinph.2006.04.018 · Cited by 189
Brunner, C., Naeem, M., Leeb, R., Graimann, B., & Pfurtscheller, G. (2007) · Pattern Recognition Letters · doi:10.1016/j.patrec.2007.01.002 · Cited by 187
Lansner, A. (2009) · Trends in Neurosciences · doi:10.1016/j.tins.2008.12.002 · Cited by 183
Darwin, C. J. (1997) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(97)01097-8 · Cited by 186
Blind Source Separation Combining Independent Component Analysis and Beamforming
Saruwatari, H., Kurita, S., Takeda, K., Itakura, F., Nishikawa, T., & Shikano, K. (2003) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/s1110865703305104 · Cited by 185
Evaluation of blind signal separation method using directivity pattern under reverberant conditions
Kurita, S., Saruwatari, H., Kajita, S., Takeda, K., & Itakura, F. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2000.861203 · Cited by 185
EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis
Žvokelj, M., Zupan, S., & Prebil, I. (2016) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2016.01.046 · Cited by 184
Artoni, F., Fanciullacci, C., Bertolucci, F., Panarese, A., Makeig, S., Micera, S., & Chisari, C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.013 · Cited by 189
Functional network connectivity in the behavioral variant of frontotemporal dementia
Filippi, M., Agosta, F., Scola, E., Canu, E., Magnani, G., Marcone, A., Valsasina, P., Caso, F., Copetti, M., Comi, G., et al. (2013) · Cortex · doi:10.1016/j.cortex.2012.09.017 · Cited by 177
Takamiya, A., Hirano, J., Yamagata, B., Takei, S., Kishimoto, T., & Mimura, M. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00001 · Cited by 184
The auditory organization of speech and other sources in listeners and computational models
Cooke, M. P., & Ellis, D. P. W. (2001) · Speech Communication · doi:10.1016/s0167-6393(00)00078-9 · Cited by 183
Vecchio, F., Miraglia, F., Marra, C., Quaranta, D., Vita, M. G., Bramanti, P., & Rossini, P. M. (2014) · Journal of Alzheimer's Disease · doi:10.3233/jad-132087 · Cited by 182
Semiblind spatial ICA of fMRI using spatial constraints
Lin, Q., Liu, J., Zheng, Y., Liang, H., & Calhoun, V. D. (2009) · Human Brain Mapping · doi:10.1002/hbm.20919 · Cited by 177
Amygdala-hippocampal dynamics during salient information processing
Zheng, J., Anderson, K. L., Leal, S. L., Shestyuk, A. Y., Gülşen, G., Mnatsakanyan, L., Vadera, S., Hsu, F. P. K., Yassa, M. A., Knight, R. T., et al. (2017) · Nature Communications · doi:10.1038/ncomms14413 · Cited by 190
A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies
Puce, A., & Hämäläinen, M. (2017) · Brain Sciences · doi:10.3390/brainsci7060058 · Cited by 194
Wavelet analysis in neurodynamics
Павлов, А. Н., Hramov, A. E., Koronovskii, A. A., Sitnikova, E. Y., Makarov, V. A., & Ovchinnikov, A. A. (2012) · Uspekhi Fizicheskih Nauk · doi:10.3367/ufnr.0182.201209a.0905 · Cited by 183
Stone, J. V., Porrill, J., Porter, N. R., & Wilkinson, I. D. (2002) · NeuroImage · doi:10.1006/nimg.2001.0986 · Cited by 182
Kaneshiro, B., Guimaraes, M. P., Kim, H.-S., Norcia, A. M., & Suppes, P. (2015) · PLoS ONE · doi:10.1371/journal.pone.0135697 · Cited by 183
Filippi, M., Valsasina, P., Misci, P., Falini, A., Comı, G., & Rocca, M. A. (2012) · Human Brain Mapping · doi:10.1002/hbm.21514 · Cited by 194
Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis
Hyvärinen, A., Ramkumar, P., Parkkonen, L. T., & Hari, R. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.08.028 · Cited by 181
Recursive self-organizing maps
Voegtlin, T. (2002) · Neural Networks · doi:10.1016/s0893-6080(02)00072-2 · Cited by 181
Independent Component Analysis Based on Nonparametric Density Estimation
Boscolo, R., Pan, H., & Roychowdhury, V. (2004) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2003.820667 · Cited by 181
Jordan, D., Ilg, R., Riedl, V., Schorer, A., Grimberg, S., Neufang, S., Omerovic, A., Berger, S., Untergehrer, G., Preibisch, C., et al. (2013) · Anesthesiology · doi:10.1097/aln.0b013e3182a7ca92 · Cited by 180
Removing artifacts from electrocardiographic signals using independent components analysis
Barros, A. K., Mansour, A., & Ohnishi, N. (1998) · Neurocomputing · doi:10.1016/s0925-2312(98)00056-3 · Cited by 180
Optimal population coding by noisy spiking neurons
Tkačik, G., Prentice, J., Balasubramanian, V., & Schneidman, E. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1004906107 · Cited by 178
Independent Component Analysis
(2004) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.826380 · Cited by 180
Survey of sparse and non‐sparse methods in source separation
O'Grady, P. D., Pearlmutter, B. A., & Rickard, S. (2005) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.20035 · Cited by 179
Sadhu, A., Narasimhan, S., & Antoni, J. (2017) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.03.001 · Cited by 179
Rabbi, M. F., Pizzolato, C., Lloyd, D. G., Carty, C. P., Devaprakash, D., & Diamond, L. E. (2020) · Scientific Reports · doi:10.1038/s41598-020-65257-w · Cited by 180
Automatic Classification of Musical Instrument Sounds
Herrera, P., Peeters, G. G., & Dubnov, S. (2003) · Journal of New Music Research · doi:10.1076/jnmr.32.1.3.16798 · Cited by 179
Acoustic Beamforming for Hearing Aid Applications
Doclo, S., Gannot, S., Moonen, M., & Spriet, A. (2010) · doi:10.1002/9780470487068.ch9 · Cited by 179
Pfabigan, D. M., Alexopoulos, J., Bauer, H., & Sailer, U. (2010) · Psychophysiology · doi:10.1111/j.1469-8986.2010.01136.x · Cited by 190
Jebelli, H., Hwang, S., & Lee, S. H. (2017) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000719 · Cited by 183
Learning the higher-order structure of a natural sound
Bell, A. J., & B, T. J. S. A. (1996) · Network Computation in Neural Systems · doi:10.1088/0954-898x/7/2/005 · Cited by 178
Self-citationWessel, J. R., & Aron, A. R. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.3456-13.2013 · Cited by 187
Brain Activity-Based Image Classification From Rapid Serial Visual Presentation
Bigdely-Shamlo, N., Vankov, A., Ramirez, R. R., & Makeig, S. (2008) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2008.2003381 · Cited by 177
Brain-Computer Interfacing: An Introduction
Rao, R. P. N. (2019) · Cited by 181
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
Spectral characteristics of resting state networks
Niazy, R. K., Xie, J., Miller, K., Beckmann, C. F., & Smith, S. M. (2011) · Progress in brain research · doi:10.1016/b978-0-444-53839-0.00017-x · Cited by 175
Decoding Individual Finger Movements from One Hand Using Human EEG Signals
Liao, K., Xiao, R., Gonzalez, J., & Ding, L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085192 · Cited by 179
McKeown, M. J. (2000) · NeuroImage · doi:10.1006/nimg.1999.0518 · Cited by 175
Abnormal thalamocortical activity in patients with Complex Regional Pain Syndrome (CRPS) Type I
Walton, K. D., Dubois, M. Y., & Llinás, R. R. (2010) · Pain · doi:10.1016/j.pain.2010.02.023 · Cited by 176
Machine learning and data science in soft materials engineering
Ferguson, A. L. (2017) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/aa98bd · Cited by 178
β-Bursts Reveal the Trial-to-Trial Dynamics of Movement Initiation and Cancellation
Wessel, J. R. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.1887-19.2019 · Cited by 173
Calhoun, V. D., Adalı, T., & Pekar, J. J. (2004) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2004.09.004 · Cited by 173
Validation of SOBI components from high-density EEG
Tang, A. C., Sutherland, M. T., & McKinney, C. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.11.027 · Cited by 173
Robust Blind Source Separation by Beta Divergence
Minami, M., & Eguchi, S. (2002) · Neural Computation · doi:10.1162/089976602760128045 · Cited by 173
Learning multidimensional signal processing
Knutsson, H. E., Borga, M., & Landelius, T. (2002) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1998.711968 · Cited by 175
Discovering Spike Patterns in Neuronal Responses
Fellous, J., Tiesinga, P. H. E., Thomas, P. J., & B, T. J. S. A. (2004) · Journal of Neuroscience · doi:10.1523/jneurosci.4649-03.2004 · Cited by 172
EEG-Informed fMRI: A Review of Data Analysis Methods
Abreu, R., Leal, A., & Figueiredo, P. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00029 · Cited by 171
ICA of functional MRI data: an overview.
Calhoun, V. D., Adalı, T., Hansen, L. K., Larsen, J. O., & Pekar, J. J. (2003) · Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) · Cited by 171
Metadata
- Authors
- Bell, A. J., & Sejnowski, T. J.
- Affiliations
- Howard Hughes Medical Institute (US)
- Howard Hughes Medical Institute, Computational Neurobiology Laboratory, The Salk Institute, 10010 N. Torrey Pines Road, La Jolla, CA 92037 USA and Department of Biology, University of California, San Diego, La Jolla, CA 92093 USA
- ORCID
- Bell, Anthony J.
- Venue
- Neural Computation
- Publisher
- MIT Press
- Published
- 1995-11
- Volume
- 7
- Issue
- 6
- Pages
- 1129-1159
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2108384452
- Cited by
- 8,986
- References
- 52
- Citations by year
- 2026: 151 · 2025: 242 · 2024: 264 · 2023: 286 · 2022: 329 · 2021: 375 · 2020: 349 · 2019: 372 · 2018: 343 · 2017: 334 · 2016: 346 · 2015: 357 · 2014: 365 · 2013: 352 · 2012: 328
- FWCI
- 130.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neural Computation (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Office of Naval Research
- Topics
- Physical Sciences › Computer Science › Signal Processing › Blind Source Separation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- information maximization, blind source separation, blind deconvolution, self-organized learning, nonlinear neural networks, higher-order statistics, redundancy reduction, principal component analysis, cocktail party problem, time delay dependence, blind signal processing
- MeSH terms
- Algorithms, Humans, Learning, Neurons, Probability, Problem Solving, Speech, Models, Statistical, Neural Networks, Computer
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