RIKEN's New Research Center for Brain Science: RIKEN CBS Website


Faculty Detail / 研究室詳細

Andrzej Cichocki, Ph.D., Dr.Sc.

- Our goal is to develop novel technologies for the analysis of massive data and processing of real time brain signals and neuroimages.

Advanced Brain Signal Processing

Senior Team Leader

Computational neuroscience, Dynamic tensor analysis, Brain/human computer interactions

Andrzej  Cichocki

Research Area

The main objective of the laboratory is develop novel artificial intelligence (AI) and machine learning (ML) technologies for analysis and processing of massive multi-modal biomedical data and for computational (neuro)science in order to model and simulate of complex mechanisms and phenomena. Cichocki Laboratory is developing novel algorithms and software for tensor decompositions and tensor networks including multilinear Independent Component Analysis (ICA), non-negative matrix/tensor factorization (NMF/NTF), and Sparse Component Analysis (SCA). The laboratory develops innovative algorithms and software for tensor networks and deep neural networks to simulate and understand complex systems and to process massive large-scale multidimensional data sets (e.g., feature extraction, classification, clustering, anomaly detection).

Selected Publications View All

  1. 1

    Yokota T, Lee N, and Cichocki A: "Robust multilinear tensor rank estimation using higher order singular value decomposition and information criteria", IEEE Transactions on Signal Processing, 65(5), 1196-1206 (2017)

  2. 2

    Thiyam D.B., Cruces S, Olias J, and Cichocki A.: "Optimization of Alpha-Beta Log-Det Divergences and their Application in the Spatial Filtering of Two Class Motor Imagery Movements", Entropy , 19(3), 89 (2017)

  3. 3

    Cichocki A, Lee N, Oseledets I.V., Phan A-H., Zhao Q, and Mandic D. : "Tensor Networks for Dimensionality Reduction and Large-Scale Optimization: Part 1 Low-Rank Tensor Decompositions", Foundation and Trends in Machine Learning, 9(4-5), 249-429 (2016)

  4. 4

    Yokota T, Zhao Q, and Cichocki A.: "Smooth PARAFAC decomposition for tensor completion", IEEE Trans. Signal Processing , 64(20), 5423-5436 (2016)

  5. 5

    Zhou S, Allison B.Z., Kübler A, Cichocki A, Wang X, and Jin J. : "Effects of background music on objective and subjective performance measures in an auditory BCI", Frontiers in Computational Neuroscience , 10(105) (2016)

  6. 6

    Lee N, and Cichocki A.: "Regularized computation of approximate pseudoinverse of large matrices using low-rank tensor train decompositions", SIAM J. Matrix Analysis Applications, 37(2), 598-623 (2016)

  7. 7

    Zhao Q, Zhou G, Zhang L, Cichocki A, and Amari S. : "Bayesian robust tensor factorization for incomplete multiway data.", IEEE Transactions on Neural Networks and Learning Systems, 27(4), 736-748 (2016)

  8. 8

    Zhou G, Zhao Q, Zhang Y, Adali T, Xie S, and Cichocki A. : "Linked component analysis from matrices to high order tensors: Applications to biomedical data", Proceedings of the IEEE, 104(2), 310-331 (2016)

  9. 9

    Cichocki A, Mandic D, Caiafa C, Phan A-H, Zhou G, Zhao Q, and De Lathauwer L.: "Tensor Decompositions for Signal Processing Applications. From Two-way to Multiway Component Analysis", IEEE Signal Processing Magazine, 32(2), 145-163 (2015)

  10. 10

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

  11. 11

    Zhou G, Cichocki A, Zhang Y, and Mandic D. : "Group component analysis for multiblock data: common and individual feature extraction", IEEE Transactions on Neural Networks and Learning Systems, 27(11), 2426-2439 (2015)

  12. 12

    Caiafa C, and Cichocki A.: "Stable, Robust, and Super Fast Reconstruction of Tensor Using Multi-Way Projections", IEEE Trans. Signal Processing, 63(3), 780-793 (2015)

  13. 13

    Ma J, Zhang Y, Cichocki A, and Matsuno F: "A Novel EOG/EEG Hybrid Human-Machine Interface Adopting Eye Movements and ERPs: Application to Robot Control.", IEEE Trans. Biomedical Engineering, 62(3), 876-889 (2015)

  14. 14

    Zhao Q, Caiafa CF, Mandic D, Chao ZC, Nagasaka Y, Fujii N, Zhang L, and Cichocki A: "Higher-Order Partial Least Squares (HOPLS): A Generalized Multi-Linear Regression Method.", IEEE Trans Pattern Anal Mach Intell (2012)

  15. 15

    Vialatte FB, Maurice M, Dauwels J, and Cichocki A: "Steady-state visually evoked potentials: focus on essential paradigms and future perspectives.", Prog Neurobiol, 90(4), 418-38 (2010)

  16. 16

    Cichocki A, and Phan AH: "Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations.", IEICE Trans. Fundamentals, E92-A(3), 708-721. (2009)

  17. 17

    Dauwels JHG, Vialatte FB, and Cichocki A: "A Comparative Study of Synchrony Measures for the Early Detection of Alzheimer's Disease Based on EEG", Lecture Notes in Computer Science, 4984, 112--125 (2008)

  18. 18

    Cichocki A, Shishkin SL, Musha T, Leonowicz Z, Asada T, and Kurachi T: "EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease.", Clin Neurophysiol, 116(3), 729-37 (2005)

  19. 19

    Cichocki A, and Amari S: "Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. (550 pages)", monograph Wiley (2003)