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  Dr. Andrzej Cichocki 

Laboratory for Advanced Brain Signal Processing
Brain Science Institute
RIKEN

2-1 Hirosawa, Wako-shi,
Saitama, 351-0198,
Japan
Phone:
Fax:
E-mail:
+81-48-467-9668
+81-48-467-9686
 
Selected publications related to Tensor analysis, Non-invasive Brain Machine Interface, Neurofeedback, SCA, ICA, NMF, BSS, MBD and their applications
 

Tensor decomposition and analysis

  1. A Cichocki, R Zdunek, A-H Phan, S Amari Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation. In: Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation, John Wiley & Sons, Ltd, (2009). [bibtex]


  2. A Cichocki, S Cruces, S Amari Log-Determinant Divergences Revisited: Alpha-Beta and Gamma Log-Det Divergences. Entropy, 17(5), 2988-3034, (2015).[bibtex]


  3. N Lee, A Cichocki Regularized Computation of Approximate Pseudoinverse of Matrices Using Low-Rank Tensor Train Decompositions. CoRR, arXiv:1506.01959, (2015).[bibtex]


  4. Q Zhao, L Zhang, A Cichocki Bayesian Sparse Tucker Models for Dimension Reduction and Tensor Completion. CoRR, arXiv:1505.02343, (2015).[bibtex]


  5. G Zhou, A Cichocki, S Xie Decomposition of Big Tensors With Low Multilinear Rank. CoRR, arXiv:1412.1885, (2015).[bibtex]


  6. T Yokota, Q Zhao, C Li, A Cichocki Smooth PARAFAC Decomposition for Tensor Completion. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), submitted, (2015).[bibtex]


  7. A Cichocki, C Mandic, AH Phan, C Caiafa, G Zhou, Q Zhao, L De Lathauwer Tensor Decompositions for Signal Processing Applications. From two-way to multiway component analysis. IEEE Signal Processing Magazine, 32(2), 145-163, (2015).[bibtex]


  8. A Cichocki Era of Big Data Processing: A New Approach via Tensor Networks and Tensor Decompositions. In: International Workshop on Smart Info-Media Systems in Asia (SISA-2013), (2013). [bibtex]


  9. A Cichocki Tensor Networks for Big Data Analytic and Large-Scale Optimization Problems. In: Second Int. Conference on Engineering and Computational Schematics (ECM2013), (2013). [bibtex]


  10. C Caiafa, A Cichocki Computing Sparse Representations of Multidimensional Signals Using Kronecker Bases. Neural Computation, 25(1), 186-220, (2013).[bibtex]


  11. C Caiafa, A Cichocki Multidimensional compressed sensing and their applications. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(6), 355-380, (2013). [bibtex]


  12. C Caiafa, A Cichocki Tensor decomposition tools for multidimensional compressed sensing. In: Signal Processing with Adaptive Sparse Structured Representations, SPARS2013, July 8-11, EPFL Lausanne, Switzerland, (2013). [bibtex]


  13. C Caiafa, A Cichocki Block Sparse Representations of Tensors Using Kronecker Bases. In: Proceedings of 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2709-2712, (2012). [bibtex]


  14. A-H Phan, P Tichavsky, A Cichocki On Fast Computation of Gradients for CANDECOMP/PARAFAC Algorithms. CoRR, arXiv:1204.1586, (2012).[bibtex]


  15. A-H Phan, P Tichavsky, A Cichocki Low Complexity Damped Gauss-Newton Algorithms for CANDECOMP/PARAFAC. SIAM Journal on Matrix Analysis and Applications, 34(1), 126-147, (2013).[bibtex]


  16. Q Zhao, C Caiafa, D Mandic, Z Chao, Y Nagasaka, N Fujii, L Zhang, A Cichocki Higher-Order Partial Least Squares (HOPLS): A Generalized Multi-linear Regression Method. IEEE Transactions on Pattern Analysis and Machine Intelligence, (In press), (2013).[bibtex]


  17. Q Zhao, C Caiafa, D Mandic, L Zhang, T Ball, A Schulze-Bonhage, A Cichocki Multilinear Subspace Regression: An Orthogonal Tensor Decomposition Approach. In: Proceedings of the 2011 Conference Neural Information Processing Systems, 1269-1277, (2011). [bibtex]


  18. G Zhou, A Cichocki, S Xie Fast Nonnegative Matrix/Tensor Factorization Based on Low-Rank Approximation. IEEE Transactions on Signal Processing, 60(6), 2928-2940, (2012).[bibtex]


  19. G Zhou, A Cichocki Fast and unique Tucker decompositions via Multiway Blind Source Separation. Bulletin of the Polish Academy of Sciences: Technical Sciences, 60(1), 389-407, (2012).[bibtex]


  20. G Zhou, Z He, Y Zhang, Q Zhao, A Cichocki Canonical Polyadic Decomposition: From 3-way to N-way. In: Proceeding of the 8th International Conference on Computational Intelligence and Security (CIS-2012), (2012). [bibtex]


  21. C Latchoumane, F Vialatte, J Sole-Casals, M Maurice, S Wimalaratna, N Hudson, J Jeong, A Cichocki Multiway array decomposition analysis of EEGs in Alzheimer's disease. Journal of Neuroscience Methods, 207(1), 41-50, (2012).[bibtex]


  22. F Cong, A-H Phan, Q Zhao, T Huttunen-Scott, J Kaartinen, T Ristaniemi, H Lyytinen, A Cichocki Benefits of Multi-domain Feature of Mismatch Negativity Extracted by Non-negative Tensor Factorization from EEG Collected by Low-Density Array. International Journal of Neural Systems, 22(6), 1-19, (2012).[bibtex]


  23. F Cong, A-H Phan, P Astikainen, Q Zhao, J Hietanen, T Ristaniemi, A Cichocki Multi-domain Feature of Event-Related Potential Extracted by Nonnegative Tensor Factorization: 5 vs 14 Electrodes EEG Data. Lecture Notes in Computer Science 7191, Latent Variable Analysis and Signal Separation, 502-510, (2012). [bibtex]


  24. F Cong, A Nandi, Z He, A Cichocki, T Ristaniemi Fast and Effective Model Order Selection Method to Determine the Number of Sources in a Linear Transformation Model. In: Proceedings of the 2012 European Signal Processing Conference, EUSIPCO-2012, 1870-1874, (2012). [bibtex]


  25. F Cong, A-H Phan, Q Zhao, A Nandi, V Alluri, P Toiviainen, H Poikonen, M Huotilainen, A Cichocki, T Ristaniemi Analysis of Ongoing EEG Elicited by Natural Music Stimuli Using Nonnegative Tensor Factorization. Proc. The 2012 European Signal Processing Conference (EUSIPCO-2012), 494-498, (2012).[bibtex]


  26. G Zhou, A Cichocki Canonical Polyadic Decomposition Based on a Single Mode Blind Source Separation. IEEE Signal Processing Letters, 19(8), 523-526, (2012).[bibtex]


  27. A Onishi, A-H Phan, K Matsuoka, A Cichocki Tensor classification for P300-based brain computer interface. In: Proceedings of 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, 581-584, (2012). [bibtex]


  28. A-H Phan, A Cichocki, P Tichavsky, D Mandic, K Matsuoka On Revealing Replicating Structures in Multiway Data: A Novel Tensor Decomposition Approach. Lecture Notes in Computer Science 7191, Latent Variable Analysis and Signal Separation, 207-305, (2012). [bibtex]


  29. A Brockmeier, J Principe, A-H Phan, A Cichocki A greedy algorithms for model selection of tensor decompositions. In: Machine Learning for Signal Processing, (draft) ICASSP, (2013). [bibtex]


  30. A-H Phan, A Cichocki, P Tichavsky, R Zdunek, S Lehky From Basis Components to Complex Structural Patterns. In: Machine Learning for Signal Processing, (draft) ICASSP, (2013). [bibtex]


  31. A-H Phan, A Cichocki, P Tichavsky, G Luta, A Brockmeier Tensor completion through multiple Kronecker product decomposition. In: Machine Learning for Signal Processing, (draft) ICASSP, (2013). [bibtex]


  32. Q Zhao, G Zhou, T Adali, L Zhang, A Cichocki Kernel-based tensor Partial Least Squares for reconstruction of limb movements. In: Machine Learning for Signal Processing, (draft) ICASSP, (2013). [bibtex]


  33. G Zhou, A Cichocki, S Xie Common and Individual Features Analysis: Beyond Canonical Correlation Analysis. CoRR, arXiv:1212.3913, (2013).[bibtex]


  34. F Cong, A-H Phan, P Astikainen, Q Zhao, Q Wu, J Hietanen, T Ristaniemi, A Cichocki Multi-domain Feature Extraction for Small Event-related Potentials through Nonnegative Multi-way Array Decomposition from Low Dense Array EEG. International Journal of Neural Systems, 23(2), 1350006, (2013).[bibtex]


  35. A Cichocki Tensors Decompositions: New Concepts for Brain Data Analysis?. Journal of Control Measurement, and System Integration, 7, 507-517, (2011).[bibtex]


  36. T Yokota, A Cichocki, Y Yamashita Linked PARAFAC/CP tensor decomposition and its fast implementation for multi-block tensor analysis. Lecture Notes in Computer Science 7665, Neural Information Processing, 84-91, (2012). [bibtex]


  37. A Cichocki, S Cruces, S Amari Generalized Alpha-Beta Divergences and Their Application to Robust Nonnegative Matrix Factorization. Entropy, 13(12), 134-170, (2011).[bibtex]
    Entropy Best Paper Award 2015 (first prize)


  38. A-H Phan, A Cichocki Seeking an Appropriate Alternative Least Squares Algorithm for Nonnegative Tensor Factorizations. Neural Computing and Applications, 21(4), 623-637, (2012).[bibtex]


  39. A-H Phan, A Cichocki PARAFAC Algorithms for Large-scale Problems. Neurocomputing, 74(11), 1970-1984, (2011).[bibtex]


  40. A-H Phan, A Cichocki Extended HALS Algorithm for Nonnegative Tucker Decomposition and its Applications for Multi-Way Analysis and Classification. Neurocomputing, 74(11), 1956-1969, (2011).[bibtex]


  41. Y Zhang, G Zhou, Q Zhao, A Onishi, J Jin, X Wang, A Cichocki Multiway Canonical Correlation Analysis for Frequency Components Recognition in SSVEP-Based BCIs. Lecture Notes in Computer Science 7062, Neural Information Processing, 287-295, (2011). [bibtex]


  42. I Kopriva, M Hadzija, Hadzija Popovic, M Korolija, A Cichocki Rational Variety Mapping for Contrast-Enhanced Nonlinear Unsupervised Segmentation of Multispectral Images of Unstained Specimen. The American Journal of Pathology, 179(2), 547-554, (2011).[bibtex]


  43. A-H Phan, P Tichavsky, A Cichocki Fast Damped Gauss-newton Algorithm for Sparse and Nonnegative Tensor Factorization. In: Proceedings of the 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1988-1991, (2011). [bibtex]


  44. C Caiafa, A Cichocki Generalizing the column-row matrix decomposition to multi-way arrays. Linear Algebra and its Applications, 433(3), 557-573, (2010).[bibtex]


  45. A-H Phan, A Cichocki Tensor Decompositions for Feature Extraction and Classification of High Dimensional Datasets. Nonlinear Theory and Its Applications, IEICE, 1(1), 37-68, (2010).[bibtex]


  46. Z He, A Cichocki, S Xie, K Choi Detecting the Number of Clusters in n-way Probabilistic Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(11), 2006-2021, (2010).[bibtex]


  47. A-H Phan, A Cichocki, T Vu-Dinh Nonnegative DEDICOM Based on Tensor Decompositions for Social Networks Exploration. Australian Journal of Intelligent Information Processing Systems, 12(1), 10-15, (2010).[bibtex]


  48. A-H Phan, A Cichocki, R Zdunek, T Vu-Dinh Novel Alternating Least Squares Algorithms for Nonnegative Matrix and Tensor Factorizations. Lecture Notes in Computer Science 6443, Neural Information Processing. Theory and Algorithms, 262-269, (2010). [bibtex]


  49. A Cichocki, A-H Phan Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E92-A(3), 708-721, (2009).[bibtex]


  50. A-H Phan, A Cichocki Tensor Decompositions for Very Large Scale Problems. Internal Report LABSP, (2009). [bibtex]
    (see also A-H Phan, A Cichocki, T Vu-Dinh Nonnegative DEDICOM Based on Tensor Decompositions for Social Networks Exploration. Australian Journal of Intelligent Information Processing Systems, 12(1), 10-15, (2010).


  51. A Cichocki, R Zdunek, S Amari Nonnegative Matrix and Tensor Factorization. IEEE Signal Processing Magazine, 25(1), 142-145, (2009).
    [bibtex]
    (see also A-H Phan, A Cichocki, T Vu-Dinh Nonnegative DEDICOM Based on Tensor Decompositions for Social Networks Exploration. Australian Journal of Intelligent Information Processing Systems, 12(1), 10-15, (2010).


  52. A Cichocki, Y Washizawa, T Rutkowski, H Bakardjian, A-H Phan, S Choi, H Lee, Q Zhao, Z Liqing, Y Li Noninvasive BCIs: Multi-way Signal Processing Array Decompositions. Computer, 41(10), 34-42, (2008).[bibtex]


  53. A Cichocki, A-H Phan, C Caiafa Flexible HALS Algorithms for Sparse Non-negative Matrix/Tensor Factorization. In: Proceedings of 2008 IEEE International Workshop on Machine Learning for Signal Processing, 73-78, (2008). [bibtex]


  54. Y-D Kim, A Cichocki, S Choi Nonnegative Tucker Decomposition with Alpha-divergence. In: Proceedings of the 2008 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1829-1832, (2008). [bibtex]


  55. A-H Phan, A Cichocki Fast and Efficient Algorithms for Nonnegative Tucker Decomposition. Lecture Notes in Computer Science 5264, Advances in Neural Networks - ISNN 2008, 772-782, (2008). [bibtex]


  56. A Cichocki, R Zdunek, S Amari Hierarchical ALS Algorithms for Nonnegative Matrix and 3D Tensor Factorization. Lecture Notes in Computer Science 4666, Independent Component Analysis and Signal Separation, 169-176, (2007). [bibtex]


  57. A Cichocki, M Jankovic, R Zdunek, S Amari Sparse Super Symmetric Tensor Factorization. Lecture Notes in Computer Science 4984, Neural Information Processing, 781-790, (2008). [bibtex]


  58. A Cichocki, A-H Phan, R Zdunek, L-Q Zhang Flexible Component Analysis for Sparse, Smooth, Nonnegative Coding or Representation. Lecture Notes in Computer Science 4984, Neural Information Processing, 811-820, (2008). [bibtex]


  59. A Cichocki, R Zdunek, S Choi, R Plemmons, S Amari Novel Multi-layer Non-negative Tensor Factorization with Sparsity Constraints. Lecture Notes in Computer Science 4432, Adaptive and Natural Computing Algorithms, 271-280, (2007). [bibtex]


  60. A Cichocki, R Zdunek Regularized Alternating Least Squares Algorithms for Non-negative Matrix/Tensor Factorizations. Lecture Notes in Computer Science 4493, Advances in Neural Networks – ISNN 2007, 793-802, (2007). [bibtex]


  61. A Cichocki, R Zdunek, S Choi, R Plemmons, S Amari Non-negative Tensor Factorization Using Alpha and Beta Divergences. In: Proceedings of 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, 1393-1396, (2007). [bibtex]


  62. H Lee, Y Kim, A Cichocki, S Choi Nonnegative Tensor Factorization for Continuous EEG Classification. International Journal of Neural Systems, 17(4), 305-317, (2007).[bibtex]



Non-invasive Brain Machine Interface and Neurofeedback

  1. A Cichocki, Y Washizawa, T Rutkowski, H Bakardjian, A-H Phan, S Choi, H Lee, Q Zhao, Z Liqing, Y Li Noninvasive BCIs: Multi-way Signal Processing Array Decompositions. Computer, 41(10), 34-42, (2008).[bibtex]


  2. Y Tomita, F Vialatte, G Dreyfus, Y Mitsukura, H Bakardjian, A Cichocki Bimodal BCI using simultaneously NIRS and EEG. IEEE Transactions on Biomedical Engineering, (99), accepted, (2014).[bibtex]


  3. J Jin, I Daly, Y Zhang, W Xingyu, A Cichocki An optimized ERP Brain-computer interface based on facial expression changes. Journal of Neural Engineering, (2014).[bibtex]


  4. Y Zhang, G Zhou, J Jin, Q Zhao, X Wang, A Cichocki Aggregation Of Sparse Linear Discriminant Analyses For Event-Related Potential Classification In Brain-Computer Interface. International Journal of Neural Systems, 24(1), 1450003 (15 pages), (2014).[bibtex]


  5. Y Zhang, G Zhou, J Jin, X Wang, A Cichocki Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis. International Journal of Neural Systems, 24(3), 1450013 (14 pages), (2014).[bibtex]


  6. J Jin, E Sellers, Y Zhang, I Daly, X Wang, A Cichocki Whether generic model works for rapid ERP-based BCI calibration. Journal of Neuroscience Methods, 212(1), 94-99, (2013).[bibtex]


  7. M Elgendi, J Dauwels, B Rebsamen, R Shukla, Y Putra, J Gamez, N ZePing, B Ho, N Prasad, D Aggarwal, A Nair, V Mishuhina, F-B Vialatte, M Constable, A Cichocki, C Latchoumane, J Jeong, D Thalmann, N Magnenat-Thalmann From Auditory and Visual to Immersive Neurofeedback: Application to Diagnosis of Alzheimer's Disease. , Neural Computation, Neurodevices, and Neural Prosthesis, in press, (2013). [bibtex]


  8. Y Zhang, G Zhou, Q Zhao, J Jin, X Wang, A Cichocki Spatial-temporal discriminant analysis for ERP-based brain-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21(2), 233-243, (2013).[bibtex]


  9. Y Zhang, G Zhou, J Jin, M Wang, X Wang, A Cichocki L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21(6), 887-896, (2013).[bibtex]


  10. J Jin, B Allison, T Kaufmann, A Kubler, Y Zhang, X Wang, A Cichocki The Changing Face of P300 BCIs: A Comparison of Stimulus Changes in a P300 BCI Involving Faces, Emotion, and Movement. PLoS One, 7(11), 1-10, (2012).[bibtex]


  11. F Vialatte, J Dauwels, T Musha, A Cichocki Audio representations of multi-channel EEG: a new tool for diagnosis of brain disorders. American Journal of Neurodegenerative Disease, 1(3), 292-304, (2012).[bibtex]


  12. Y Zhang, Q Zhao, J Jin, X Wang, A Cichocki A Novel BCI Based on ERP Components Sensitive to Configural Processing of Human Faces. Journal of Neural Engineering, 9(2), online, (2012).[bibtex]


  13. Q Zhao, Y Zhang, A Onishi, A Cichocki An Affective BCI Using Multiple ERP Components Associated to Facial Emotion Processing. SpringerBriefs in Electrical and Computer Engineering, Brain-Computer Interface Research, 61-72, (2013). [bibtex]


  14. A Onishi, A-H Phan, K Matsuoka, A Cichocki Tensor classification for P300-based brain computer interface. In: Proceedings of 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, 581-584, (2012). [bibtex]


  15. H Bakardjian, T Tanaka, A Cichocki Emotional Faces Boost up Steady-state Visual Responses for Brain-Computer Interface. NeuroReport, 22(3), 121-125, (2011).[bibtex]


  16. Y Nam, H Kang, Q Zhao, A Cichocki, S Choi Mind Flipper: An EEG-based Brain Computer Interface for Page-turning during Presentation. Australian Journal of Intelligent Information Processing Systems, 11(3), online, (2010).[bibtex]



NMF/NTF/NTD (Nonnegative Matrix/Tensor Factorization, Nonnegative Tucker Decomposition)

  1. A Cichocki, S Cruces, S Amari Generalized Alpha-Beta Divergences and Their Application to Robust Nonnegative Matrix Factorization. Entropy, 13(12), 134-170, (2011).[bibtex]


  2. A Cichocki, S Amari Families of Alpha-Beta-and Gamma-Divergences: Flexible and Robust Measures of Similarities. Entropy, 12(6), 1532-1568, (2010).[bibtex]
    Entropy Best Paper Award 2014 (second prize)


  3. A Cichocki, A-H Phan Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E92-A(3), 708-721, (2009).[bibtex]


  4. A Cichocki, R Zdunek, S Amari Nonnegative Matrix and Tensor Factorization. IEEE Signal Processing Magazine, 25(1), 142-145, (2009).[bibtex]


  5. A Cichocki, H-K Lee, Y-D Kim, S Choi Nonnegative Matrix Factorization with Alpha-divergence. Pattern Recognition Letters, 29(9), 1433-1440, (2008).[bibtex]


  6. A Cichocki, Y Washizawa, T Rutkowski, H Bakardjian, A-H Phan, S Choi, H Lee, Q Zhao, Z Liqing, Y Li Noninvasive BCIs: Multi-way Signal Processing Array Decompositions. Computer, 41(10), 34-42, (2008).[bibtex]


  7. A Cichocki, A-H Phan, C Caiafa Flexible HALS Algorithms for Sparse Non-negative Matrix/Tensor Factorization. In: Proceedings of 2008 IEEE International Workshop on Machine Learning for Signal Processing, 73-78, (2008). [bibtex]


  8. A Cichocki, R Zdunek Multi-layer Nonnegative Matrix Factorization using Projected Gradient Approaches. International Journal of Neural Systems, 17(6), 431-446, (2007).[bibtex]


  9. A Cichocki, R Zdunek, S Amari Hierarchical ALS Algorithms for Nonnegative Matrix and 3D Tensor Factorization. Lecture Notes in Computer Science 4666, Independent Component Analysis and Signal Separation, 169-176, (2007). [bibtex]


  10. A Cichocki, M Jankovic, R Zdunek, S Amari Sparse Super Symmetric Tensor Factorization. Lecture Notes in Computer Science 4984, Neural Information Processing, 781-790, (2008). [bibtex]


  11. A Cichocki, A-H Phan, R Zdunek, L-Q Zhang Flexible Component Analysis for Sparse, Smooth, Nonnegative Coding or Representation. Lecture Notes in Computer Science 4984, Neural Information Processing, 811-820, (2008). [bibtex]


  12. A Cichocki, R Zdunek, S Choi, R Plemmons, S Amari Novel Multi-layer Non-negative Tensor Factorization with Sparsity Constraints. Lecture Notes in Computer Science 4432, Adaptive and Natural Computing Algorithms, 271-280, (2007). [bibtex]


  13. A Cichocki, R Zdunek Regularized Alternating Least Squares Algorithms for Non-negative Matrix/Tensor Factorizations. Lecture Notes in Computer Science 4493, Advances in Neural Networks – ISNN 2007, 793-802, (2007). [bibtex]


  14. A Cichocki, R Zdunek, S Choi, R Plemmons, S Amari Non-negative Tensor Factorization Using Alpha and Beta Divergences. In: Proceedings of 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, 1393-1396, (2007). [bibtex]


  15. A Cichocki, R Zdunek, S Amari Csiszar’s Divergences for Non-negative Matrix Factorization: Family of New Algorithms. Lecture Notes in Computer Science 3889, Independent Component Analysis and Blind Signal Separation, 32-39, (2006). [bibtex]


  16. A Cichocki, S Amari, R Zdunek, R Kompass, G Hori, Z He Extended SMART Algorithms for Non-negative Matrix Factorization. Lecture Notes in Computer Science 4029, Artificial Intelligence and Soft Computing – ICAISC 2006, 548-562, (2006). [bibtex]


  17. A Cichocki, R Zdunek, S Amari New Algorithms for Non-negative Matrix Factorization in Applications to Blind Source Separation. In: Proceedings of 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing, 621-624, (2006). [bibtex]


  18. A Cichocki, R Zdunek Multi-layer Nonnegative Matrix Factorization. Electronics Letters, 42(16), 947, (2006).[bibtex]


  19. A Cichocki, R Zdunek Multi-layer Nonnegative Matrix Factorization using Projected Gradient Approaches. International Journal of Neural Systems, 17(6), 431-446, (2007).[bibtex]


  20. Z Chen, A Cichocki, T Rutkowski Constrained Non-Negative Matrix Factorization Method for EEG Analysis in Early Detection of Alzheimer's Disease. In: Proceedings of 2006 IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP), 893-896, (2006). [bibtex]


  21. Y-D Kim, A Cichocki, S Choi Nonnegative Tucker Decomposition with Alpha-divergence. In: Proceedings of the 2008 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1829-1832, (2008). [bibtex]


  22. H Lee, Y Kim, A Cichocki, S Choi Nonnegative Tensor Factorization for Continuous EEG Classification. International Journal of Neural Systems, 17(4), 305-317, (2007).[bibtex]


  23. A-H Phan, A Cichocki Fast and Efficient Algorithms for Nonnegative Tucker Decomposition. Lecture Notes in Computer Science 5264, Advances in Neural Networks - ISNN 2008, 772-782, (2008). [bibtex]


  24. A-H Phan, A Cichocki, K-S Nguyen Simple and Efficient Algorithm for Distributed compressed sensing. In: Proceedings of the 2008 IEEE International Workshop on Machine Learning for Signal Processing, 61-66, (2008). [bibtex]


  25. T Rutkowski, R Zdunek, A Cichocki Multichannel EEG Brain Activity Pattern Analysis in Time-frequency Domain with Nonnegative Matrix Factorization Support. International Congress Series, 1301, 266-269, (2007).[bibtex]


  26. R Zdunek, A Cichocki Nonnegative Matrix Factorization with Quadratic Programming. Neurocomputing, 71(10-12), 2309-2320, (2008).[bibtex]


  27. R Zdunek, A Cichocki Nonnegative Matrix Factorization with Constrained Second-order Optimization. Signal Processing, 87(8), 1904-1916, (2007).[bibtex]


  28. R Zdunek, A Cichocki Non-negative Matrix Factorization with Quasi-Newton Optimization. Lecture Notes in Computer Science 4029, Artificial Intelligence and Soft Computing – ICAISC 2006, 870-879, (2006). [bibtex]


  29. R Zdunek, A Cichocki Improved M-FOCUSS Algorithm with Overlapping Blocks for Locally Smooth Sparse Signals. IEEE Transactions on Signal Processing, 56(10), 4752-4761, (2008).[bibtex]


  30. R Zdunek, A Cichocki Fast Nonnegative Matrix Factorization Algorithms Using Projected Gradient Approaches for Large-Scale Problems. Computational Intelligence and Neuroscience, 2008, ID 939567, (2008).[bibtex]



SCA (Sparse Component Analysis)

  1. A Cichocki, Y Li, P-G Georgiev, S Amari Beyond ICA: Robust Sparse Signal Representations. In: Proceedings of 2004 IEEE International Symposium on Circuits and Systems, 684-687, (2004). [bibtex]


  2. Z He, A Cichocki K-Subspace Clustering and its Application in Sparse Component Analysis. In: The 14th European Symposium on Artificial Neural Networks, (2006). [bibtex]


  3. Z He, A Cichocki K-EVD Clustering and Its Applications to Sparse Component Analysis. Lecture Notes in Computer Science 3889, Independent Component Analysis and Blind Signal Separation, 90-97, (2006). [bibtex]


  4. P Georgiev, F Theis, A Cichocki Sparse Component Analysis and Blind Source Separation of Underdetermined Mixtures. IEEE Transactions on Neural Networks, 16(4), 992-996, (2005).[bibtex]


  5. P-G Georgiev, F Theis, A Cichocki Optimization algorithms for sparse representations and applications. Optimization algorithms for sparse representations and applications 82, Mulitscale Optimization Methods and Applications, 85-99, (2005). [bibtex]


  6. P Georgiev, F Theis, A Cichocki, H Bakardjian Sparse Component Analysis: A New Tools for Data Mining. Springer Optimization and Its Applications 7, Data Mining in Biomedicine, 91-116, (2005). [bibtex]


  7. P-G Georgiev, F-J Theis, A Cichocki Blind source separation and sparse component analysis of overcomplete mixtures,. In: Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP2004), 493-496, (2004). [bibtex]


  8. P-G Georgiev, A Cichocki Sparse Component Analysis of Overcomplete Mixtures by Improved Basis Pursuit Method. In: Proceedings of 2004 IEEE International Symposium on Circuits and Systems, 37-40, (2004). [bibtex]


  9. J Karvanen, A Cichocki Measuring sparseness of noisy signals,. In: Proceedings of 4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), 125-130, (2003). [bibtex]


  10. Y Li, A Cichocki, S Amari Analysis of Sparse Representation and Blind Source Separation. Neural Computation, 16(6), 1193-1234, (2004).[bibtex]


  11. Y Li, A Cichocki, S Amari, S Shishkin, J Cao, F Gu Sparse Representation and its Applications in Blind Source Separation. In: Advances in Neural Information Processing Systems, 241-248, (2004). [bibtex]


  12. Y Li, A Cichocki, S Amari Sparse component analysis for blind source separation with less sensors than sources,. In: Proceedings of 4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), 89-94, (2003). [bibtex]


  13. Y Li, A Cichocki Sparse representation of images using alternating linear programming. In: Proceedings of The Seventh International Symposium on Signal Processing and Its Applications (ISSPA2003), 57-60, (2003). [bibtex]


  14. F-J Theis, P-G Georgiev, A Cichocki Robust Overcomplete Matrix Recovery for Sparse Sources using a Generalized Hough Transform. In: Proceedings of 12th European Symposium on Artificial Neural Networks, 343-348, (2004). [bibtex]


  15. Y Washizawa, A Cichocki On-line K-plane Clustering Learning Algorithm for Sparse Component Analysis. In: Proceedings of 2006 IEEE Conference on Acoustics, Speech, and Signal Processing (ICASSP), 681-684, (2006). [bibtex]



ICA - very old papers before I joined RIKEN

  1. A Cichocki, R Unbehauen Robust neural networks with on-line learning for blind identification and blind separation of sources. IEEE Transaction on Circuits and Systems - I: Fundamental Theory and Applications, 43, 894-906, (1996).[bibtex]


  2. A Cichocki, R Bogner, L Moszczynski Improved adaptive algorithms for blind separation of sources. In: Proc. of Conference on Electronic Circuits and Systems, KKTOiUE, 647-652, (1995). [bibtex]


  3. A Cichocki, R Unbehauen, E Rummert Robust learning algorithm for blind separation of signals. Electronics Letters, 30(17), 1386-1387, (1994).[bibtex]


  4. A Cichocki, R Unbehauen, L Moszczynski, E Rummert A new on-line adaptive algorithm for blind separation of source signals. In: 1994 Int. Symposium on Artificial Neural Networks ISANN-94, 406-411, (1994). [bibtex]



ICA/BSS - papers about Independent Component Analysis and Blind Source Separation

  1. S Amari, T-P Chen, A Cichocki Nonholonomic orthogonal learning algorithms for blind source separation. NeuroComputation, 12, 1463-1484, (2000).[bibtex]


  2. S Amari, T-P Chen, A Cichocki Stability analysis of adaptive blind source separation. Neural Networks, 10(8), 1345-1351, (1997).[bibtex]


  3. S Amari, T Chen, A Cichocki Non-holonomic constraints in learning blind source separation. In: Progress in Connectionist-Based Information Systems (ICONIP-97), 633-636, (1997). [bibtex]


  4. S Amari, A Cichocki, H-H Yang A New Learning Algorithm for Blind Signal Separation. Advances in Neural Information Processing Systems, 8, 757-763, (1996).[bibtex]


  5. S Amari, A Cichocki, H-H Yang Recurrent neural networks for blind separation of sources. In: Proc. Int. Symposium on Nonlinear Theory and its Applications - NOLTA'95, 37-42, (1995). [bibtex]


  6. M Adachi, K Aihara, A Cichocki Separation of mixed patterns by a chaotic neural network. In: 1996 International Symposium on Nonlinear Theory and its Applications - NOLTA'96, Proceedings, 93-96, (1996). [bibtex]


  7. A-D Back, A Cichocki Input variable selection using independent component analysis and higher order statistics. In: Proc. of the First International Workshop on Independent Component Analysis and Signal Separation - ICA'99, 203-208, (1999). [bibtex]


  8. A-K Barros, A Cichocki A fixed point algorithm for independent component analysis which use a priori information. In: 5th Brazilian Symposium on Neural Networks, (1998). [bibtex]


  9. A Belouchrani, A Cichocki, K Abed A Blind identification and separation technique via multi-layer neural networks. In: Progress in Neural Information Processing - ICONIP'96, Proceedings, 1195-1200, (1996). [bibtex]


  10. J Cao, N Murata, S Amari, A Cichocki, T Takeda A robust approach to independent component analysis of signals with high-level noise measurements. IEEE Transactions on Neural Networks, 14, 631-645, (2003).[bibtex]


  11. J Cao, N Murata, S Amari, A Cichocki, T Takeda Independent component analysis for single-trial MEG data decomposition and single-dipole source localization. Neurocomputing, 49, 255-277, (2002).[bibtex]


  12. J Cao, N Murata, S Amari, A Cichocki, T Takeda MEG data analysis based on ICA approach with pre- & post-processing techniques. In: Proceedings of 1998 International Symposium on Nonlinear Theory and its Applications (NOLTA-98), 287-290, (1998). [bibtex]


  13. J Cao, A Cichocki Blind source separation algorithm based on 4th-order self- and cross-cumulants criterion applied for EEG data analysis. In: Proceedings of 1997 International Symposium on Nonlinear Theory and its Applications (NOLTA-97), 1005-1008, (1997). [bibtex]


  14. A Cichocki Blind Signal Processing Methods for Analyzing Multichannel Brain Signals. International Journal of Bioelectromagnetism, 6(1), online, (2004).[bibtex]


  15. A Cichocki, S Amari Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. In: , Wiley, (2003). [bibtex]


  16. A Cichocki The laboratory for Advanced Brain Signal Processing - RIKEN BSI: Why it is, and how it came to be. Journal of Signal Processing, 7(4), 295-302, (2003).[bibtex]


  17. A Cichocki, P Georgiev Blind Source Separation Algorithms with Matrix Constraints. IEICE Trans. Fundamentals, E86-A(3), 522-531, (2003).[bibtex]


  18. A Cichocki Blind Source Separation: New Tools for Extraction of Source Signals and Denoising. In: Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III, 11-25, (2005). [bibtex]


  19. A Cichocki, T-M Rutkowski, K Siwek Blind signal extraction of signals with specified frequency band. In: Neural Networks for Signal Processing XII: Proceedings of the 2002 IEEE Signal Processing Society Workshop, 515-524, (2002). [bibtex]


  20. A Cichocki, T Rutkowski, A-K Barros, S Oh A blind extraction of temporally correlated but statistically dependent acoustic signals. In: Neural Networks for Signal Processing X: Proceedings of the 2000 IEEE Signal Processing Society Workshop (NNSP2000), 455-464, (2000). [bibtex]


  21. A Cichocki, R Thawonmas On-line algorithm for blind signal extraction of arbitrary distributed, but temporally correlated sources using second order statistics. Neural Processing Letters, 12, 91-98, (2000).[bibtex]


  22. A Cichocki, J Karhunen, W Kasprzak, R Vigario Neural networks for blind separation with unkonown number of sources. Neurocomputing, 14, 55-93, (1999).[bibtex]


  23. A Cichocki, J Cao, S Amari, N Murata, T Takeda, H Endo Enhancement and blind identification of magnetoencephalographic signals using independent component analysis. In: Proc of th 11th Int. Conference on Biomagentism BIOMAG-98, (1998). [bibtex]


  24. A Cichocki, S Douglas, S Amari, P Mierzejewski Independent component analysis for noisy data. In: Proc. of International Workshop on Independence and Artificial Neural Networks, 52-58, (1998). [bibtex]


  25. A Cichocki, I Sabala, S Amari Intelligent neural networks for blind signal separation with unknown number of sources. In: Proc. of Conf. Engineering of Intelligent Systems, ESI-98, 148-154, (1998). [bibtex]


  26. A Cichocki Blind identification and separation of noisy source signals Neural networks approaches. In: ISCIE Japan, (1998). [bibtex]


  27. A Cichocki, I Sabala, S Amari Intelligent neural networks for blind signal separation with unknown number of sources. In: Proc. of Conf. Engineering of Intelligent Systems, ESI-98, 148-154, (1998). [bibtex]


  28. A Cichocki, S Douglas, S Amari, P Mierzejewski Independent component analysis for noisy data. In: Proc. of International Workshop on Independence and Artificial Neural Networks, 52-58, (1998). [bibtex]


  29. A Cichocki On-line blind signal extraction methods exploiting a priori knowledge of the previously extracted signals. In: Proc. the 1997 IEEE Workshop on Nonlinear Signal and Image Processing, (1997). [bibtex]


  30. A Cichocki Dual cascade networks for blind signal extraction. In: Proc. the 1997 International Conference on Neural Networks (ICNN'97), 2135-2140, (1997). [bibtex]


  31. A Cichocki, R-E Bogner, L Moszczynski, K Pope Modified Herault-Jutten algorithms for blind separation of sources. Digital Signal Processing, 7(2), 80-93, (1997).[bibtex]


  32. A Cichocki, B Orsier, A Back, S Amari On-line adaptive algorithms in non-stationary environments using modified conjugate gradient approach. In: Neural Networks for Signal Processing, Proc. of 1997 IEEE Workshop on NNSP, 316-325, (1997). [bibtex]


  33. A Cichocki, I Sabala, S Choi, B Orsier, R Szupiluk Self adaptive independent component analysis for sub-Gaussian and super-Gaussian mixtures with unknown number of sources and additive noise. In: Proceedings of 1997 International Symposium on Nonlinear Theory and its Applications (NOLTA-97), 731-734, (1997). [bibtex]


  34. A Cichocki, R Thawonmas, S Amari Sequential blind signal extraction in order specified by stochastic properties. Electronics Letters, 33(1), 64-65, (1997).[bibtex]


  35. A Cichocki, W Kasprzak, S Amari Adaptive approach to blind source separation with cancellation of additive and convolutional noise. In: Third International Conf. on Signal Processing, ICSP'96, 412-415, (1996). [bibtex]


  36. A Cichocki, W Kasprzak, S Amari Neural network approach to blind separation and enhancement of images. Signal Processing 1, Signal Processing VIII. Theories and Applications., 579-582, (1996). [bibtex]


  37. A Cichocki, W Kasprzak Nonlinear learning algorithms for blind separation of natural images. Neural Network World, 4(4), 515-523, (1996).[bibtex]


  38. A Cichocki, S Amari, M Adachi, W Kasprzak Self-adaptive neural networks for blind separation of sources. In: ISCAS-96 May 1996, 157-160, (1996). [bibtex]


  39. A Cichocki, S Amari, R Thawonmas Blind signal extraction using self-adaptive non-linear Hebbian learning rule. In: Proceedings of 1996 International Symposium on Nonlinear Theory and its Applications - NOLTA'96, 377-380, (1996). [bibtex]


  40. A Cichocki, W Kasprzak, S Amari Multi-layer neural networks with a local adaptive learning rule for blind separation of source signals. In: Proc. Int. Symposium on Nonlinear Theory and its Applications - NOLTA'95, 61-66, (1995). [bibtex]


  41. S Choi, A Cichocki, HM Park, SY Lee Blind Source Separation and Independent Component Analysis: A Review. Neural Information Processing - Letters and Reviews, 6(1), 1-57, (2005).[bibtex]


  42. S Choi, A Cichocki, L Zhang, S Amari Approximate maximum likelihood source separation using the natural gradient. IEICE Trans. Fundamentals, E86-A(1), 206-214, (2003).[bibtex]


  43. S Choi, A Cichocki, S Amari Equivariant nonstationary source separation. Neural Networks, 15, 121-130, (2002).[bibtex]


  44. S Choi, A Cichocki, A Belouchrani Second order nonstationary source separation. Journal of VLSI Signal Processing, 32(1-2), 93-104, (2002).[bibtex]


  45. S Choi, S Amari, A Cichocki Natural gradient learning for spatio-temporal decorrelation: Recurrent network. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E83-A(12), 2715-2722, (2000).[bibtex]


  46. S Choi, A Cichocki Blind separation of nonstationary sources in noisy mixtures. Electronics Letters, 36(9), 848-849, (2000).[bibtex]


  47. S Choi, A Cichocki, S Amari Flexible independent component analysis. Journal of VLSI Signal Processing, 26, 25-38, (2000).[bibtex]


  48. S Choi, A Cichocki An unsupervised hybrid network for blind separation of independent non-Gaussian source signals in multipath environment. Journal of Communications and Networks, 1(1), 19-25, (1999).[bibtex]


  49. S Choi, A Cichocki, S Amari Flexible independent component analysis. Journal of VLSI Signal Processing, 26, 25-38, (2000).[bibtex]


  50. S Choi, R Liu, A Cichocki A spurious equilibria-free learning algorithm for the blind separation of non-zero skewness signals. Neural Processing Letters, 7(2), 61-68, (1998).[bibtex]


  51. S Choi, A Cichocki A linear feedforward neural network with lateral feedback connections for blind source separation. In: IEEE Signal Processing Workshop on Higher-order Statistics, 349-353, (1997). [bibtex]


  52. S-A Crucez-Alvarez, A Cichocki, S Amari From Blind Signal Extraction to Blind Instantaneous Signal Separation: Criteria, Algorithms and Stability. IEEE Transactions on Neural Networks, 15(4), 859-873, (2004).[bibtex]


  53. S Cruces, A Cichocki, L Lathauwer Thin QR and SVD Factorizations for Simultaneous Blind Signal Extraction. In: Proceedings of 12th European Signal Processing Conference (EUSIPCO04), 217-220, (2004). [bibtex]


  54. S Cruces, A Cichocki, S Amari On a new blind signal extraction algorithm: Different criteria and stability analysis. In: IEEE Signal Processing Letters, 233-236, (2002). [bibtex]


  55. S Cruces, L Castedo, A Cichocki Robust blind source separation algorithms using cumulants. Neurocomputing, 49, 87-118, (2002).[bibtex]


  56. S Cruces, L Castedo, A Cichocki An iterative inversion method for blind source separation. In: Proc. of the First International Workshop on Independent Component Analysis and Signal Separation - ICA'99, 307-312, (1999). [bibtex]


  57. S Ding, J Huang, D Wei, A Cichocki A Near Real-Time Approach for Convolutive Blind Source Separation. IEEE Transactions on Circuits and Systems I: Regular Papers, 53(1), 114-128, (2006).[bibtex]


  58. S Ding, A Cichocki, J Huang, D Wei Blind Source Separation of Acoustic Signals in Realistic Environments Based on ICA in the Time-frequency Domain. International Journal of Pervasive Computing and Communications, 1(2), 89-100, (2005).[bibtex]


  59. S-C Douglas, A Cichocki Convergence Analysis of Local Algorithms for Blind Signal Processing. In: Neural Information Processing Systems Conference, Workshop on Blind Signal Processing, (1996). [bibtex]


  60. P Georgiev, A Cichocki Robust independent component analysis via time-delayed cumulant functions. IEICE Transactions on Fundamentals, E86-A(3), 573-579, (2003).[bibtex]


  61. A Cichocki Second-order statistics based blind source separation using a bank of subband filters. Digital Signal Processing, 13, 252-274, (2003).[bibtex]


  62. M Jafari, W Wang, J Chambers, T Hoya, A Cichocki Sequential Blind Source Separation Based Exclusively on Second-Order Statistics Developed for a Class of Periodic Signals. IEEE Transactions on Signal Processing, 54(3), 1028-1040, (2006).[bibtex]


  63. O Jahn, A Cichocki, A Ioannides, S Amari Identification and elimination of artifacts from MEG signals using efficient Independent Components Analysis. In: Proc. of th 11th Int. Conference on Biomagentism BIOMAG-98, (1998). [bibtex]


  64. Y Li, A Cichocki, S Amari Analysis of Sparse Representation and Blind Source Separation. Neural Computation, 16(6), 1193-1234, (2004).[bibtex]


  65. Y Li, J Wang, A Cichocki Blind Source Extraction from Convolutive Mixtures in Ill-conditioned Multi-input Multi-output Channels. IEEE Transactions on Circuits and Systems I: Regular Papers, 51(9), 1814-1822, (2004).[bibtex]


  66. Y Li, A Cichocki, L Zhang Blind Source Estimation of FIR Channels for Binary Sources: A Grouping Decision Approach. Signal Processing, 84(12), 2245-2263, (2004).[bibtex]


  67. Y Li, A Cichocki, L Zhang Blind separation and extraction of binary sources. IEICE Transactions on Fundamentals, E86-A(3), 580-589, (2003).[bibtex]


  68. J Karhunen, A Cichocki, W Kasprzak, P Pajunen On neural blind separation with noise suppression and redundancy reduction. International Journal of Neural Systems, 8(2), 219-237, (1997).[bibtex]


  69. W Kasprzak, A Cichocki, S Amari Blind source separation with convolutive noise cancellation. Journal of Neural Computing and Applications, 6, 127-141, (1997).[bibtex]


  70. J Qin, Y Li, A Cichocki ICA and Committee Machine-based Algorithm for Cursor Control in a BCI System. Lecture Notes in Computer Science 3496, Advances in Neural Networks – ISNN 2005, 973-978, (2005). [bibtex]


  71. W Kasprzak, A Cichocki Hidden image separation from incomplete image mixtures by independent component analysis. In: 13th Int. Conf. on Pattern Recognition, ICPR'96, 394-398, (1996). [bibtex]


  72. T Tanaka, A Cichocki Subband Decomposition Independent Component Analysis and New Performance Criteria. In: Proceedings of International Conference on Acoustics, Speech, and Signal Processing, 541-544, (2004). [bibtex]


  73. R Thwanomas, A Cichocki Blind signal extraction of arbitrary distributed but temporally correlated signals-neural network approach. IEICE Transactions, Fundamentals, E82 A(9), 1834-1844, (1999).[bibtex]


  74. R Thawonmas, A Cichocki Blind extraction of source signals with specified stochastic features. In: ICASSP-97, 3353-3357, (). [bibtex]


  75. S-A Vorobyov, A Cichocki Blind noise reduction for multi-sensory signals using ICA and subspace filtering with application to EEG analysis. Biological Cybernetics, 86(4), 293-303, (2002).[bibtex]


  76. H-H Yang, S Amari, A Cichocki Information-Theoretic Approach to Blind Separation of Sources in Non-linear Mixture. Signal Processing, 64(3), 291-300, (1998).[bibtex]


  77. L Zhang, A Cichocki, S Amari Multichannel blind deconvolution of nonminimum-phase systems using filter decomposition. IEEE Transactions on Signal Processing, 52(5), 1430-1442, (2004).[bibtex]


  78. L Zhang, A Cichocki, S Amari Self-adaptive Blind Source Separation Based on Activation Functions Adaptation. IEEE Transactions on Neural Networks, 15(2), 233-244, (2004).[bibtex]


  79. L Zhang, A Cichocki, S Amari Geometrical structures of FIR manifold and their application to multichannel blind deconvolution. Journal of VLSI for Signal Processing, 31, 31-44, (2002).[bibtex]


  80. L Zhang, A Cichocki, S Amari Natural gradient algorithm for blind separation of overdetermined mixture with additive noise. IEEE Signal Processing Letters, 6(11), 293-295, (1999).[bibtex]



Early Detection/Diagnosis of Alzheimer's Disease

  1. A Cichocki, S Shishkin, T Musha, Z Leonowicz, T Asada, T Kurachi EEG Filtering Based on Blind Source Separation (BSS) for Early Detection of Alzheimer’s Disease. Clinical Neurophysiology, 116(3), 729-737, (2005).[bibtex]


  2. F Vialatte, A Cichocki, G Dreyfus, T Musha, S-L Shishkin, R Gervais Early Diagnosis of Alzheimer's Disease by Blind Source Separation, Time Frequency Representation, and Bump Modeling of EEG Signals. 3696, Lecture Notes in Computer Science, 683-692, (2005). [bibtex]


  3. Z Chen, A Cichocki, T Rutkowski Constrained Non-Negative Matrix Factorization Method for EEG Analysis in Early Detection of Alzheimer's Disease. In: Proceedings of 2006 IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP), 893-896, (2006). [bibtex]


  4. J-H Park, S-Y Kim, C-H Kim, A Cichocki, K Kim Multiscale Entropy Analysis of EEG from Patients under Different Pathological Conditions. Fractals, 15(4), 399-404, (2007).[bibtex]


  5. WL Woon, A Cichocki, F Vialatte, T Musha Techniques for Early Detection of Alzheimer’s Disease Using Spontaneous EEG Recordings. Physiological Measurement, 28(4), 335-347, (2007).[bibtex]


  6. F Vialatte, J Sole-Casals, M Maurice, C Latchoumane, N Hudson, S Wimalaratna, J Jeong, A Cichocki Improving the Quality of EEG Data in Patients with Alzheimers Disease Using ICA. Lecture Notes in Computer Science 5507, Advances in Neuro-Information Processing, 979-986, (2009). [bibtex]


  7. F-B Vialatte, T Musha, A Cichocki Sparse Bump Sonification: a New Tool for Multichannel EEG Diagnosis of Brain Disorders. Artificial Intelligence in Medicine, (2009).[bibtex]


  8. Dauwels, J., Vialatte, F., and Cichocki, A. Diagnosis of Alzheimer's disease from EEG signals: Where are we standing? , Current Alzheimer Research, (2010). [bibtex]


  9. Dauwels, J., Vialatte, F., Musha, T., and Cichocki A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG , NeuroImage, (2010). [bibtex]


  10. Dauwels, J., Srinivasan, K., Ramasubba Reddy, M., Musha, T., Vialatte, F. B., Latchoumane, C., and Cichocki, A Slowing and loss of complexity in Alzheimer's EEG: two sides of the same coin? Physiological Measurement, 28(4), 335-347, (2007).[bibtex]


  11. M Elgendi, J Dauwels, B Rebsamen, R Shukla, Y Putra, J Gamez, N ZePing, B Ho, N Prasad, D Aggarwal, A Nair, V Mishuhina, F-B Vialatte, M Constable, A Cichocki, C Latchoumane, J Jeong, D Thalmann, N Magnenat-Thalmann From Auditory and Visual to Immersive Neurofeedback: Application to Diagnosis of Alzheimer's Disease. , Neural Computation, Neurodevices, and Neural Prosthesis, in press, (2013). [bibtex]


  12. C Latchoumane, F Vialatte, J Sole-Casals, M Maurice, S Wimalaratna, N Hudson, J Jeong, A Cichocki Multiway array decomposition analysis of EEGs in Alzheimer's disease. Journal of Neuroscience Methods, 207(1), 41-50, (2012).[bibtex]


  13. M Elgendi, J Dauwels, B Rebsamen, R Shukla, Y Putra, J Gamez, N ZePing, B Ho, N Prasad, D Aggarwal, A Nair, V Mishuhina, F-B Vialatte, M Constable, A Cichocki, C Latchoumane, J Jeong, D Thalmann, N Magnenat-Thalmann From Auditory and Visual to Immersive Neurofeedback: Application to Diagnosis of Alzheimer's Disease. , Neural Computation, Neurodevices, and Neural Prosthesis, in press, (2013). [bibtex]


  14. Hiyoshi‐Taniguchi, K., Oishi, N., Namiki, C., Miyata, J., Murai, T., Cichocki, A., & Fukuyama, H. The Uncinate Fasciculus as a Predictor of Conversion from aMCI to Alzheimer Disease. , Journal of Neuroimaging, (2014). [bibtex]


  15. Gallego-Jutglà, E., Solé-Casals, J., Vialatte, F. B., Elgendi, M., Cichocki, A., and Dauwels, J. The Uncinate Fasciculus as a Predictor of Conversion from aMCI to Alzheimer Disease. , Journal of Neural Engineering, (2015). [bibtex]


  16. Gallego Jutglà, E., Solé-Casals, J., Vialatte, F. B., Dauwels, J., and Cichocki, A. A Theta-Band EEG Based Index for Early Diagnosis of Alzheimer’s Disease , Journal of Alzheimer’s Disease, (2015). [bibtex]



EEG, BMI, attention, emotion states, pain related papers

  1. J Cao, L Zhao, A Cichocki Visualization of dynamic brain activities based on single-trial MEG and EEG data analysis. 3973, Lecture Notes in Computer Science, 531-540, (2006). [bibtex]


  2. Z Chen, S Ohara, J Cao, F Vialatte, F Lenz, A Cichocki Statistical Modeling and Analysis of Laser-Evoked Potentials of Electrocorticogram Recordings from Awake Humans. Computational Intelligence and Neuroscience, 2007, 1-24, (2007).[bibtex]


  3. F Fallani, L Astolfi, F Cincotti, D Mattia, M Marciani, S Salinari, J Kurths, S Gao, A Cichocki, A Colosimo, F Babioni Cortical Functional Connectivity Networks in Normal and Spinal Cord Injured Patients: Evaluation by Graph Analysis. Human Brain Mapping, 28(12), 1334-1346, (2007).[bibtex]


  4. A Funase, T Yagi, A-K Barros, A Cichocki, I Takumi Single Trial Method for Brain-Computer Interface. In: Proceedings of 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 5277-5281, (2006). [bibtex]


  5. A Funase, T Yagi, A Barros, A Cichocki, I Takumi Comparison of Saccade-related EEG Signal with Saccade-related Independent Component. In: Proceedings 27th Annual International Conference of the IEEE Engineering in Medicine and Biology, 7060-7063, (2005). [bibtex]


  6. A Funase, T Yagi, M Mouri, A Barros, A Cichocki, I Takumi Analysis on EEG Signals in Visually and Auditory Guided Saccade Task by FICAR. Lecture Notes in Computer Science 3889, Independent Component Analysis and Blind Signal Separation, 438-445, (2006). [bibtex]


  7. H Lee, A Cichocki, S Choi Nonnegative Matrix Factorization for Motor Imagery EEG Classification. Lecture Notes in Computer Science 4132, Artificial Neural Networks – ICANN 2006, 250-259, (2006). [bibtex]


  8. H Lee, Y Kim, A Cichocki, S Choi Nonnegative Tensor Factorization for Continuous EEG Classification. International Journal of Neural Systems, 17(4), 305-317, (2007).[bibtex]


  9. Y Li, A Cichocki, S Amari Blind Estimation of Channel Parameters and Source Components for EEG Signals: A Sparse Factorization Approach. IEEE Transactions on Neural Networks, 17(2), 419-431, (2006).[bibtex]


  10. P Martinez, H Bakardjian, A Cichocki Fully Online Multi-command Brain-Computer Interface with Visual Neurofeedback Using SSVEP Paradigm. Computational Intelligence and Neuroscience, 2007, 1-9, (2007).[bibtex]


  11. S Osowski, B Swiderski, A Cichocki, A Rysz Epileptic Seizure Characterization by Lyapunov Exponent of EEG Signal. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 26(5), 1276-1287, (2007).[bibtex]


  12. J Qin, Y Li, A Cichocki ICA and Committee Machine-based Algorithm for Cursor Control in a BCI System. Lecture Notes in Computer Science 3496, Advances in Neural Networks – ISNN 2005, 973-978, (2005). [bibtex]


  13. T Rutkowski, R Zdunek, A Cichocki Multichannel EEG Brain Activity Pattern Analysis in Time-frequency Domain with Nonnegative Matrix Factorization Support. International Congress Series, 1301, 266-269, (2007).[bibtex]


  14. T Rutkowski, A Cichocki, A Ralescu, D Mandic Emotional States Estimation from Multichannel EEG Maps. Advances in Cognitive Neurodynamics, Proceedings of the International Conference on Cognitive Neurodynamics, 695-698, (2008). [bibtex]


  15. T Rutkowski, F Vialatte, A Cichocki, D Mandic, A Barros Auditory Feedback for Brain Computer Interface Management - An EEG Data Sonification Approach. Lecture Notes in Computer Science 4253, Knowledge-Based Intelligent Information and Engineering Systems, 1232-1239, (2006). [bibtex]


  16. WL Woon, A Cichocki Novel Features for Brain Computer Interfaces. Computational Intelligence and Neuroscience, 2007, 1-7, (2007).[bibtex]



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