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

Default-Mode Network Abnormalities in Pediatric Posttraumatic Stress Disorder

Patriat, R., Birn, R. M., Keding, T. J., & Herringa, R. J. (2016) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2016.01.010 · Cited by 78

Resting-state time-varying analysis reveals aberrant variations of functional connectivity in autism

Yao, Z., Hu, B., Xie, Y., Zheng, F., Liu, G., Chen, X., & Zheng, W. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00463 · Cited by 74

Infraslow Electroencephalographic and Dynamic Resting State Network Activity

Grooms, J. K., Thompson, G. J., Pan, W.-J., Billings, J., Schumacher, E. H., Epstein, C. M., & Keilholz, S. D. (2017) · Brain Connectivity · doi:10.1089/brain.2017.0492 · Cited by 74

Structure-function relationships behind the phenomenon of cognitive resilience in neurology: insights for neuroscience and medicine.

Rudrauf, D. (2014) · Advances in Neuroscience · doi:10.1155/2014/462765 · Cited by 74

ADHD-related sex differences in fronto-subcortical intrinsic functional connectivity and associations with delay discounting

Rosch, K. S., Mostofsky, S. H., & Nebel, M. B. (2018) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-018-9254-9 · Cited by 69

Neural network of speech monitoring overlaps with overt speech production and comprehension networks: a sequential spatial and temporal ICA study

van de Ven, V., Esposito, F., & Christoffels, I. K. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.057 · Cited by 72

Generalizability of machine learning for classification of schizophrenia based on resting-state functional MRI data

Cai, X., Xie, D., Madsen, K. H., Wang, Y., Bögemann, S. A., Cheung, E. F. C., Møller, A., & Chan, R. C. K. (2020) · Human Brain Mapping · doi:10.1002/hbm.24797 · Cited by 62

Distinct EEG signatures differentiate unconsciousness and disconnection during anaesthesia and sleep

Casey, C. P., Tanabe, S., Farahbakhsh, Z., Parker, M., Bo, A., White, M., Ballweg, T., Mcintosh, A., Filbey, W., Saalmann, Y., et al. (2022) · British Journal of Anaesthesia · doi:10.1016/j.bja.2022.01.010 · Cited by 66

Specific default mode subnetworks support mentalizing as revealed through opposing network recruitment by social and semantic FMRI tasks: Mentalizing and Default-Mode Subnetworks

Hyatt, C. J., Calhoun, V. D., Pearlson, G. D., & Assaf, M. (2015) · Human Brain Mapping · doi:10.1002/hbm.22827 · Cited by 65

Learning to denoise astronomical images with U-nets

Vojtekova, A., Lieu, M., Valtchanov, I., Altieri, B., Old, L., Chen, Q., & Hroch, F. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa3567 · Cited by 63

Adverse childhood experiences are associated with self-regulation and the magnitude of the error-related negativity difference

Lackner, C. L., Santesso, D. L., Dywan, J., O’Leary, D. D., Wade, T. J., & Segalowitz, S. J. (2018) · Biological Psychology · doi:10.1016/j.biopsycho.2018.01.006 · Cited by 60

Physiological synchrony in EEG, electrodermal activity and heart rate reflects shared selective auditory attention

Stuldreher, I. V., Thammasan, N., van Erp, J. B. F., & Brouwer, A.-M. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/aba87d · Cited by 58

An Information-Theoretic Perspective on Intrinsic Motivation in Reinforcement Learning: A Survey

Aubret, A., Matignon, L., & Hassas, S. (2023) · Entropy · doi:10.3390/e25020327 · Cited by 52

Functional connectivity and cannabis use in high-risk adolescents

Houck, J. M., Bryan, A. D., & Feldstein Ewing, S. W. (2013) · The American Journal of Drug and Alcohol Abuse · doi:10.3109/00952990.2013.837914 · Cited by 50

Abnormalities in functional connectivity in borderline personality disorder: Correlations with metacognition and emotion dysregulation

Quattrini, G., Pini, L., Pievani, M., Magni, L. R., Lanfredi, M., Ferrari, C., Boccardi, M., Bignotti, S., Magnaldi, S., Cobelli, M., et al. (2019) · Psychiatry Research: Neuroimaging · doi:10.1016/j.pscychresns.2018.12.010 · Cited by 48

Distinctive Resting State Network Disruptions Among Alzheimer’s Disease, Subcortical Vascular Dementia, and Mixed Dementia Patients

Kim, H. J., Cha, J., Lee, J.-M., Shin, J. S., Jung, N.-Y., Kim, Y. J., Choe, Y. S., Lee, K. H., Kim, S. T., Kim, J. S., et al. (2016) · Journal of Alzheimer's Disease · doi:10.3233/jad-150637 · Cited by 46

Independent component analysis: fetal signal reconstruction from magnetocardiographic recordings

Comani, S., Mantini, D., Pennesi, P., Lagatta, A., & Cancellieri, G. (2004) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2003.12.005 · Cited by 46

Developmental differences in higher-order resting-state networks in Autism Spectrum Disorder

Bos, D. J., van Raalten, T. R., Oranje, B., Smits, A. R., Kobussen, N. A., Belle, J. V., Rombouts, S. A. R. B., & Durston, S. (2014) · NeuroImage: Clinical · doi:10.1016/j.nicl.2014.05.007 · Cited by 45

A novel anomaly detection method for digital twin data using deconvolution operation with attention mechanism

Li, Z., Duan, M., Xiao, B., & Yang, S. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3231923 · Cited by 45

Dementia ConnEEGtome: towards multicentric harmonization of EEG connectivity in neurodegeneration

Prado, P., Birba, A., Cruzat, J., Santamaría-García, H., Parra, M., Moguilner, S., Tagliazucchi, E., & Ibáñez, A. (2022) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2021.12.008 · Cited by 43

All about the Money ? External Performance Monitoring is Affected by Monetary, but Not by Socially Conveyed Feedback Cues in More Antisocial Individuals

Pfabigan, D. M., Alexopoulos, J., Bauer, H., Lamm, C., & Sailer, U. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00100 · Cited by 41

Characterization of fetal arrhythmias by means of fetal magnetocardiography in three cases of difficult ultrasonographic imaging

COMANI, S., LIBERATI, M., MANTINI, D., GABRIELE, E., BRISINDA, D., DI LUZIO, S., FENICI, R., & LUCA ROMANI, G. (2004) · Pacing and Clinical Electrophysiology · doi:10.1111/j.1540-8159.2004.00699.x · Cited by 38

Co-maturation of theta and low-beta rhythms during child development

Rodríguez-Martínez, E. I., Barriga-Paulino, C. I., Rojas-Benjumea, M. A., & Gómez, C. M. (2015) · Brain Topography · doi:10.1007/s10548-014-0369-3 · Cited by 36

Functional connectivity during cognitive control in children with autism spectrum disorder: an independent component analysis

Ambrosino, S., Bos, D. J., van Raalten, T. R., Kobussen, N. A., van Belle, J., Oranje, B., & Durston, S. (2014) · Journal of Neural Transmission · doi:10.1007/s00702-014-1237-8 · Cited by 29

Identifying brain networks in synaptic density PET (11C-UCB-J) with independent component analysis

Fang, X. T., Toyonaga, T., Hillmer, A. T., Matuskey, D., Holmes, S. E., Radhakrishnan, R., Mecca, A. P., van Dyck, C. H., D'Souza, D. C., Esterlis, I., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118167 · Cited by 30

EEG Signal Diversity Varies With Sleep Stage and Aspects of Dream Experience

Aamodt, A., Nilsen, A. S., Thürer, B., Moghadam, F. H., Kauppi, N., Juel, B. E., & Storm, J. F. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.655884 · Cited by 23

Linking resting-state network fluctuations with systems of coherent synaptic density: A multimodal fMRI and 11C-UCB-J PET study

Fang, X. T., Volpi, T., Holmes, S. E., Esterlis, I., Carson, R. E., & Worhunsky, P. D. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1124254 · Cited by 18

Perception and Bias in the Processing of Compound versus Phrasal Stress: Evidence from Event-Related Brain Potentials

McCauley, S. M., Hestvik, A., & Vogel, I. (2013) · Language and Speech · doi:10.1177/0023830911434277 · Cited by 19

Developmental language disorder as syntactic prediction impairment

Hestvik, A., Epstein, B., Schwartz, R. G., & Shafer, V. L. (2022) · Frontiers in Communication · doi:10.3389/fcomm.2021.637585 · Cited by 11

Event-related potentials of automatic imitation are modulated by ethnicity during stimulus processing, but not during motor execution

Rauchbauer, B., Pfabigan, D. M., & Lamm, C. (2018) · Scientific Reports · doi:10.1038/s41598-018-30926-4 · Cited by 15

Investigating human audio-visual object perception with a combination of hypothesis-generating and hypothesis-testing fMRI analysis tools

Naumer, M. J., van den Bosch, J. J. F., Wibral, M., Kohler, A., Singer, W., Kaiser, J., van de Ven, V., & Muckli, L. (2011) · Experimental Brain Research · doi:10.1007/s00221-011-2669-0 · Cited by 11

Integrating EEG and machine learning to analyze brain changes during the rehabilitation of Broca’s aphasia

Močilnik, V., Rutar Gorišek, V., Sajovic, J., Pretnar Oblak, J., Drevenšek, G., & Rogelj, P. (2024) · Sensors · doi:10.3390/s24020329 · Cited by 8

Interplay of self-other distinction and cognitive control mechanisms in a social automatic imitation task: An ERP study

Rauchbauer, B., Lorenz, C., Lamm, C., & Pfabigan, D. M. (2021) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/s13415-021-00878-w · Cited by 8

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051

Untitled work

(1998) · doi:10.1002/(sici)1097-0193(1998)6:3

Untitled work

(2003) · Cited by 1,680

Identifying global anatomical differences: Deformation-based morphometry

(1998) · doi:10.1002/(sici)1097-0193(1998)6:5/6

Deep Semi-Supervised Anomaly Detection

Ruff, L., Vandermeulen, R. A., Görnitz, N., Binder, A., Müller, E., Müller, K.-R., & Kloft, M. (2019) · arXiv · doi:10.48550/arxiv.1906.02694 · Cited by 388

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