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

The chronoarchitecture of the cerebral cortex

Bartels, A., & Zeki, S. (2005) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2005.1627 · Cited by 107

Variational Mixture of Bayesian Independent Component Analyzers

Choudrey, R., & Roberts, S. (2003) · Neural Computation · doi:10.1162/089976603321043766 · Cited by 107

Predicting Reaching Targets from Human EEG

Hammon, P. S., Makeig, S., Howard Poizner, A., Todorov, E. V., & de Sa, V. R. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.4408443 · Cited by 109

Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data

Havlicek, M., Jan, J., Brazdil, M., & Calhoun, V. D. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.063 · Cited by 106

Effects of an Alpha 7-Nicotinic Agonist on Default Network Activity in Schizophrenia

Tregellas, J. R., Tanabe, J., Rojas, D. C., Shatti, S., D, A. O. M., Johnson, L. L., Martín, L., Soti, F. S., Kem, W. R., Leonard, S. S., et al. (2010) · Biological Psychiatry · doi:10.1016/j.biopsych.2010.07.004 · Cited by 106

A Multichannel MMSE-Based Framework for Speech Source Separation and Noise Reduction

Souden, M., Araki, S., Kinoshita, K., Nakatani, T., & Sawada, H. (2013) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2013.2263137 · Cited by 106

Topographic Product Models Applied to Natural Scene Statistics

Osindero, S., Welling, M., & Hinton, G. E. (2005) · Neural Computation · doi:10.1162/089976606775093936 · Cited by 108

Multimodal Data Fusion Using Source Separation: Application to Medical Imaging

Adalı, T., Levin‐Schwartz, Y., & Calhoun, V. D. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2461601 · Cited by 105

At a Single Glance: Fast Periodic Visual Stimulation Uncovers the Spatio-Temporal Dynamics of Brief Facial Expression Changes in the Human Brain

Dzhelyova, M. P., Jacques, C., & Rossion, B. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw223 · Cited by 102

Group ICA for identifying biomarkers in schizophrenia: ‘Adaptive’ networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression

Salman, M. S., Du, Y., Lin, D., Fu, Z., Fedorov, A., Damaraju, E., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Chen, J., Mayer, A. R., Posse, S., et al. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101747 · Cited by 115

Subspace Learning for Background Modeling: A Survey

Bouwmans, T. (2009) · Recent Advances in Computer Science and Communications · doi:10.2174/1874479610902030223 · Cited by 105

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

Independent component analysis applied on gas sensor array measurement data

Kermit, M., & Tomić, O. (2003) · IEEE Sensors Journal · doi:10.1109/jsen.2002.807488 · Cited by 105

Neural networks and the separation of cosmic microwave background and astrophysical signals in sky maps

Baccigalupi, C., Bedini, L., Burigana, C., de Zotti, G., Farusi, A., Maino, D., MARIS, M., Perrotta, F., Salerno, E., Toffolatti, L., et al. (2000) · Monthly Notices of the Royal Astronomical Society · doi:10.1046/j.1365-8711.2000.03751.x · Cited by 105

Automated interictal spike detection and source localization in magnetoencephalography using independent components analysis and spatio-temporal clustering

Ossadtchi, A., Baillet, S., Mosher, J. C., Thyerlei, D., Sutherling, W., & Leahy, R. M. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2003.10.036 · Cited by 104

Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines

Cheng, S., Chen, J., & Wang, L. (2018) · Entropy · doi:10.3390/e20080583 · Cited by 104

Brain–heart interactions reveal consciousness in noncommunicating patients

Raimondo, F., Rohaut, B., Demertzi, A., Valente, M., Engemann, D. A., Salti, M., Fernandez Slezak, D., Naccache, L., & Sitt, J. D. (2017) · Annals of Neurology · doi:10.1002/ana.25045 · Cited by 106

Small-World Characteristics of Cortical Connectivity Changes in Acute Stroke

Caliandro, P., Vecchio, F., Miraglia, F., Reale, G., Marca, G. D., Torre, G. L., Lacidogna, G., Iacovelli, C., Padua, L., Bramanti, P., et al. (2016) · Neurorehabilitation and neural repair · doi:10.1177/1545968316662525 · Cited by 109

The spatial chronnectome reveals a dynamic interplay between functional segregation and integration

Iraji, A., DeRamus, T. P., Lewis, N., Yaesoubi, M., Stephen, J. M., Erhardt, E. B., Belger, A., Ford, J. M., McEwen, S. C., Mathalon, D. H., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24580 · Cited by 107

A Theoretical Perspective on Hyperdimensional Computing

Thomas, A. H., Dasgupta, S., & Rosing, T. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12664 · Cited by 128

Mining in chemometrics

Mutihac, L., & Mutihac, R. (2008) · Analytica Chimica Acta · doi:10.1016/j.aca.2008.02.025 · Cited by 104

Functionally independent components of early event-related potentials in a visual spatial attention task

Makeig, S., Westerfield, M., Townsend, J., Jung, T., Courchesne, E., & B, T. J. S. A. (1999) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.1999.0469 · Cited by 104

Shift-invariant multilinear decomposition of neuroimaging data

Mørup, M., Hansen, L. K., Arnfred, S., Lim, L., & Madsen, K. H. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.05.062 · Cited by 104

Phase-Based Dual-Microphone Robust Speech Enhancement

Aarabi, P., & Shi, G. (2004) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2004.830345 · Cited by 104

High Classification Accuracy for Schizophrenia with Rest and Task fMRI Data

Du, W., Calhoun, V. D., Li, H., Ma, S., Eichele, T., Kiehl, K. A., Pearlson, G. D., & Adalı, T. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00145 · Cited by 105

Nonlinear Blind Source Separation by Self-Organizing Maps

Pajunen, P., Hyvärinen, A., & Karhunen, J. (1996) · Cited by 104

A Bayesian approach to source separationPreprint

Knuth, K. H. (2002) · arXiv (Cornell University) · doi:10.48550/arxiv.physics/0205032 · Cited by 104

Altered resting-state intra- and inter- network functional connectivity in patients with persistent somatoform pain disorder

Zhao, Z., Huang, T., Tang, C., Ni, K., Pan, X., Yan, C., Fan, X., Xu, D., & Luo, Y. (2017) · PLOS ONE · doi:10.1371/journal.pone.0176494 · Cited by 103

Statistical feature extraction for artifact removal from concurrent fMRI-EEG recordings

Liu, Z., de Zwart, J. A., van Gelderen, P., Kuo, L.-W., & Duyn, J. H. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.042 · Cited by 97

Independent component analysis for document restoration

Tonazzini, A., Bedini, L., & Salerno, E. (2004) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s10032-004-0121-8 · Cited by 103

EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments

Raza, H., Prasad, G., & Li, Y. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.07.028 · Cited by 103

Single-Channel Speech Separation Using Soft Mask Filtering

Radfar, M., & Dansereau, R. M. (2007) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2007.904233 · Cited by 103

A fully automatic ocular artifact suppression from EEG data using higher order statistics: Improved performance by wavelet analysis

Ghandeharion, H., & Erfanian, A. (2010) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2010.04.010 · Cited by 103

Common and distinct brain networks underlying verbal and visual creativity

Zhu, W., Chen, Q., Xia, L., Beaty, R. E., Yang, W., Tian, F., Sun, J., Cao, G., Zhang, Q., Chen, X., et al. (2017) · Human Brain Mapping · doi:10.1002/hbm.23507 · Cited by 108

Impact of autocorrelation on functional connectivity

Arbabshirani, M. R., Damaraju, E., Phlypo, R., Plis, S., Allen, E. A., Ma, S., Mathalon, D. H., Preda, A., Vaidya, J. G., Adalı, T., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.07.045 · Cited by 101

Characteristics of the default mode functional connectivity in normal ageing and Alzheimer's disease using resting state fMRI with a combined approach of entropy-based and graph theoretical measurements

Toussaint, P., Maiz, S., Coynel, D., Doyon, J., Messé, A., de Souza, L. C., Sarazin, M., Perlbarg, V., Habert, M., & Benali, H. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.08.003 · Cited by 105

Changes of Functional and Effective Connectivity in Smoking Replenishment on Deprived Heavy Smokers: A Resting-State fMRI Study

Ding, X., & Lee, S.-W. (2013) · PLoS ONE · doi:10.1371/journal.pone.0059331 · Cited by 103

Mining EEG–fMRI using independent component analysis

Eichele, T. (2009) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2008.12.018 · Cited by 105

Altered Default Network Activity in Obesity

Tregellas, J. R., Wylie, K. P., Rojas, D. C., Tanabe, J., Martin, J., Kronberg, E., Cordes, D., & Cornier, M. (2011) · Obesity · doi:10.1038/oby.2011.119 · Cited by 104

Blind Extraction of Dominant Target Sources Using ICA and Time-Frequency Masking

Sawada, H., Araki, S., Mukai, R., & Makino, S. (2006) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2006.872599 · Cited by 102

Two Independent Frontal Midline Theta Oscillations during Conflict Detection and Adaptation in a Simon-Type Manual Reaching Task

Töllner, T., Wang, Y., Makeig, S., Müller, H. J., Jung, T., & Gramann, K. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.1752-16.2017 · Cited by 102

Blind Source Separation for Convolutive Mixtures: A Unified Treatment

Buchner, H., Aichner, R., & Kellermann, W. (2004) · Springer eBooks · doi:10.1007/1-4020-7769-6_10 · Cited by 102

Coordinate Dependence of Variability Analysis

Sternad, D., Park, S., Müller, H., & Hogan, N. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000751 · Cited by 102

Interictal high-frequency oscillations generated by seizure onset and eloquent areas may be differentially coupled with different slow waves

Nonoda, Y., Miyakoshi, M., Ojeda, A., Makeig, S., Juhász, C., Sood, S., & Asano, E. (2016) · Clinical Neurophysiology · doi:10.1016/j.clinph.2016.03.022 · Cited by 103

Mapping white-matter functional organization at rest and during naturalistic visual perception

Lynch, L. K., Lu, K., Wen, H., & Liu, Z. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.005 · Cited by 104

Neural speech restoration at the cocktail party: Auditory cortex recovers masked speech of both attended and ignored speakers

Brodbeck, C. J., Jiao, A., Hong, L. E., & Simon, J. Z. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000883 · Cited by 101

Emotion self-regulation training in major depressive disorder using simultaneous real-time fMRI and EEG neurofeedback

Zotev, V. S., Mayeli, A., Misaki, M., & Bodurka, J. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102331 · Cited by 105

Thalamocortical excitability modulation guides human perception under uncertainty

Kosciessa, J. Q., Lindenberger, U., & Garrett, D. D. (2021) · Nature Communications · doi:10.1038/s41467-021-22511-7 · Cited by 102

Separate cortical networks involved in music perception: preliminary functional MRI evidence for modularity of music processing

Schmithorst, V. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.12.006 · Cited by 101

Tonic and phasic electroencephalographic dynamics during continuous compensatory tracking

Huang, R., Jung, T., Arnaud Delorme, A., & Makeig, S. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.10.036 · Cited by 101

Magneto- and electroencephalographic manifestations of reward anticipation and delivery

Doñamayor, N., Schoenfeld, M. A., & Münte, T. F. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.04.038 · Cited by 102

A geometric algorithm for overcomplete linear ICA

Theis, F. J., Lang, E. W., & Puntonet, C. G. (2003) · Neurocomputing · doi:10.1016/j.neucom.2003.09.008 · Cited by 101

Information Geometry on Complexity and Stochastic Interaction

Ay, N. (2015) · Entropy · doi:10.3390/e17042432 · Cited by 96

An Advanced Scheme for Range Ambiguity Suppression of Spaceborne SAR Based on Blind Source Separation

Chang, S., Deng, Y., Zhang, Y., Zhao, Q., Wang, R., & Zhang, K. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3184709 · Cited by 100

Spectral and Spatial Classification of Hyperspectral Images Based on ICA and Reduced Morphological Attribute Profiles

Falco, N., Benediktsson, J. A., & Bruzzone, L. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2436335 · Cited by 99

Developmental evaluation of atypical auditory sampling in dyslexia: Functional and structural evidence

Lizarazu, M., Lallier, M., Molinaro, N., Bourguignon, M., Paz‐Alonso, P. M., Lerma‐Usabiaga, G., & Carreiras, M. (2015) · Human Brain Mapping · doi:10.1002/hbm.22986 · Cited by 99

Blind Signal Separation Methods for InSAR: The Potential to Automatically Detect and Monitor Signals of Volcanic Deformation

Gaddes, M., Hooper, A. J., Bagnardi, M., Inman, H., & Albino, F. (2018) · Journal of Geophysical Research Solid Earth · doi:10.1029/2018jb016210 · Cited by 99

The nonlinear PCA criterion in blind source separation: Relations with other approaches

Karhunen, J., Pajunen, P., & Erkki Oja, A. (1998) · Neurocomputing · doi:10.1016/s0925-2312(98)00046-0 · Cited by 99

Nonholonomic Orthogonal Learning Algorithms for Blind Source Separation

Амари, Ш.-И., Chen, T., & Cichocki, A. S. (2000) · Neural Computation · doi:10.1162/089976600300015466 · Cited by 99

Inferring the eigenvalues of covariance matrices from limited, noisy data

Everson, R., & Roberts, S. (2000) · IEEE Transactions on Signal Processing · doi:10.1109/78.847792 · Cited by 99

Spectral decomposition of a 4th-order covariance tensor: Applications to diffusion tensor MRI

Basser, P. J., & Pajevic, S. (2006) · Signal Processing · doi:10.1016/j.sigpro.2006.02.050 · Cited by 99

Blind deconvolution of images using optimal sparse representations

Bronstein, M. M., Bronstein, A. M., Zibulevsky, M., & Zeevi, Y. Y. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.847322 · Cited by 99

An on-line algorithm for blind source separation on speech signals

Murata, N. (1998) · Cited by 99

Thalamic deep brain stimulation in traumatic brain injury: a phase 1, randomized feasibility study

Schiff, N. D., Giacino, J. T., Butson, C. R., Choi, E. Y., Baker, J., O'Sullivan, K. P., Janson, A. P., Bergin, M. J. G., Bronte-Stewart, H. M., Chua, J., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02638-4 · Cited by 95

ICA and Gabor representation for facial expression recognition

Buciu, I., Kotropoulos, C. L., & Pitas, I. (2004) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2003.1246815 · Cited by 98

Bed posture classification for pressure ulcer prevention

Yousefi, R., Ostadabbas, S., Faezipour, M., Farshbaf, M., Nourani, M., Tamil, L. S., & Pompeo, M. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091813 · Cited by 98

Integrating independent component analysis-based denoising scheme with neural network for stock price prediction

Lu, C.-J. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.03.012 · Cited by 98

Multi-way clustering of microarray data using probabilistic sparse matrix factorization

Dueck, D., Morris, Q., & Frey, B. J. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti1041 · Cited by 98

Working memory-related frontal theta activity is decreased under acute stress

Gärtner, M., Rohde-Liebenau, L., Grimm, S., & Bajbouj, M. (2014) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2014.02.009 · Cited by 103

Electrical Signal Source Separation Via Nonnegative Tensor Factorization Using On Site Measurements in a Smart Home

Figueiredo, M. B., Ribeiro, B., & de Almeida, A. (2014) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2013.2278596 · Cited by 97

Parallel processing in speech perception with local and global representations of linguistic context

Brodbeck, C. J., Bhattasali, S., Heredia, A. A. L. C., Resnik, P., Simon, J. Z., & Lau, E. F. (2022) · eLife · doi:10.7554/elife.72056 · Cited by 103

Automatic Bayesian Classification of Healthy Controls, Bipolar Disorder, and Schizophrenia Using Intrinsic Connectivity Maps From fMRI Data

Arribas, J. I., Calhoun, V. D., & Adalı, T. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2080679 · Cited by 100

A blind signal separation method for multiuser communications

Castedo, L., Escudero, C. J., & Dapena, A. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.575706 · Cited by 97

Sparsifying Transform Learning With Efficient Optimal Updates and Convergence Guarantees

Ravishankar, S., & Bresler, Y. (2015) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2015.2405503 · Cited by 99

An Augmented Two-Layer Model Captures Nonlinear Analog Spatial Integration Effects in Pyramidal Neuron Dendrites

Jadi, M. P., Behabadi, B. F., Poleg-Polsky, A., Schiller, J., & Mel, B. W. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2312671 · Cited by 96

Language Prediction Is Reflected by Coupling between Frontal Gamma and Posterior Alpha Oscillations

Wang, L., Peter Hagoort, A., & Jensen, O. B. (2017) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01190 · Cited by 94

Catecholamines alter the intrinsic variability of cortical population activity and perception

Pfeffer, T. A., Avrămiea, A.-E., Nolte, G., Engel, A. K., Linkenkaer‐Hansen, K., & Donner, T. H. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2003453 · Cited by 98

Body Movement Selectively Shapes the Neural Representation of Musical Rhythms

Chemin, B., Mouraux, A., & Nozaradan, S. (2014) · Psychological Science · doi:10.1177/0956797614551161 · Cited by 96

Online entropy manipulation: stochastic information gradient

Deniz Erdogmus, B., Hild, K. E., & Príncipe, J. C. (2003) · IEEE Signal Processing Letters · doi:10.1109/lsp.2003.814400 · Cited by 96

A resampling approach to estimate the stability of one-dimensional or multidimensional independent components

Meinecke, F. C., Ziehe, A., Kawanabe, M., & Müller, K. R. (2002) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2002.805480 · Cited by 96

Blind Source Separation

Naik, G. R., & Wang, W. (2014) · Signals and communication technology · doi:10.1007/978-3-642-55016-4 · Cited by 96

Columnar Resolution of Blood Volume and Oximetry Functional Maps in the Behaving Monkey

Vanzetta, I., Slovin, H., Omer, D. B., & Grinvald, A. (2004) · Neuron · doi:10.1016/j.neuron.2004.04.004 · Cited by 96

Temporal Expectation and Attention Jointly Modulate Auditory Oscillatory Activity in the Beta Band

Todorovic, A., Schoffelen, J.-M., van Ede, F., Maris, E., & de Lange, F. P. (2015) · PLOS ONE · doi:10.1371/journal.pone.0120288 · Cited by 84

Simple Method for High-Performance Digit Recognition Based on Sparse Coding

Labusch, K., Barth, E., & Martinetz, T. (2008) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005830 · Cited by 93

Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA–WT during Working Memory Tasks

Al-Qazzaz, N. K., Ali, S. H. M., Ahmad, S. A., Islam, M., & Escudero, J. (2017) · Sensors · doi:10.3390/s17061326 · Cited by 94

Independent component ordering in ICA time series analysis

Cheung, Y., & Xu, L. (2001) · Neurocomputing · doi:10.1016/s0925-2312(00)00358-1 · Cited by 95

A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia

Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Adalı, T., Pearlson, G. D., Yang, H., Sponheim, S. R., White, T. J. H., & Calhoun, V. D. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.069 · Cited by 93

An electrophysiological signal that precisely tracks the emergence of error awareness

Murphy, P. R., Robertson, I. H., Allen, D., Hester, R., & O'Connell, R. G. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00065 · Cited by 94

A Metabolomic Approach to the Study of Wine Micro-Oxygenation

Arapitsas, P., Scholz, M., Vrhovšek, U., Blasi, S., Bartolini, A. B., Masuero, D., Perenzoni, D., Rigo, A., & Mattivi, F. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037783 · Cited by 94

Altered Functional Connectivity between Emotional and Cognitive Resting State Networks in Euthymic Bipolar I Disorder Patients

Lois, G., Linke, J. O., & Wessa, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0107829 · Cited by 100

EEG-Based Detection of Driver Emergency Braking Intention for Brain-Controlled Vehicles

Teng, T., Bi, L., & Liu, Y. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2740427 · Cited by 94

Disentanglement of local field potential sources by independent component analysis

Makarov, V. A., Makarova, J., & Herreras, Ó. (2010) · Journal of Computational Neuroscience · doi:10.1007/s10827-009-0206-y · Cited by 94

Real-time sound source localization and separation for robot audition

Nakadai, K., Okuno, H. G., & Kitano, H. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2002-118 · Cited by 94

Measure projection analysis: A probabilistic approach to EEG source comparison and multi-subject inference

Bigdely-Shamlo, N., Mullen, T. R., Kreutz-Delgado, K., & Makeig, S. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.01.040 · Cited by 93

Two-Microphone Separation of Speech Mixtures

Pedersen, M. S., Wang, D., Larsen, J. O., & Kjems, U. (2008) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2007.911740 · Cited by 94

From single-trial EEG to brain area dynamics

Arnaud Delorme, A., Makeig, S., Fabre‐Thorpe, M., & B, T. J. S. A. (2002) · Neurocomputing · doi:10.1016/s0925-2312(02)00415-0 · Cited by 94

On the relationship between auditory cognition and speech intelligibility in cochlear implant users: An ERP study

Finke, M., Büchner, A., Ruigendijk, E., Meyer, M. E., & Sandmann, P. (2016) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2016.05.019 · Cited by 92

Neural networks for blind separation with unknown number of sources

Cichocki, A. S., Karhunen, J., Kasprzak, W., & Vigário, R. (1999) · Neurocomputing · doi:10.1016/s0925-2312(98)00091-5 · Cited by 94

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