Centers & Labs

RIKEN Brain Science Institute

Laboratory for Advanced Brain Signal Processing

Laboratory Head: Andrzej Cichocki (Ph.D.)
Andrzej  Cichocki(Ph.D.)

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 and prediction).

Main Research Field


Related Research Fields

Engineering / Biological Sciences


  • Tensor networks and tensor decomposition
  • Multi-linear blind source separation
  • Brain Computer Interface (BCI)
  • Linked multi-way component analysis
  • Deep Neural Networks

Selected Publications

  1. Cichocki, A., Phan, A. H., Zhao, Q., Lee, N., Oseledets, I., Sugiyama, M., & Mandic, D. P.:
    "Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives"
    Foundations and Trends® in Machine Learning, 9(6), 431-673.(2017)
  2. Cichocki A, Mandic D, Caiafa C, Phan A-H, Zhou G, Zhao Q, 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)
  3. Cichocki A, Cruces S, Amari S.:
    "Log-Determinant Divergences Revisited: Alpha-Beta and Gamma Log-Det Divergences"
    Entropy, 17(5), 2988-3034. (2015).
  4. Caiafa C, Cichocki A.:
    "Stable, Robust, and Super Fast Reconstruction of Tensor Using Multi-Way Projections"
    IEEE Trans. Signal Processing, 63(3), 780-793. (2015)
  5. Zhao Q, Caiafa C, Mandic D, Chao Z, Nagasaka Y, Fujii N, Zhang L, Cichocki A.:
    "Higher-Order Partial Least Squares (HOPLS): A Generalized Multilinear Regression Method"
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(7), 1660-1673. (2013).
  6. Dauwels J, Vialatte F, Musha T, Cichocki A.:
    "A Comparative Study of Synchrony Measures for Early Diagnosis of Alzheimer's Disease Based on EEG"
    NeuroImage, 49(1), 668-693. (2010).
  7. Vialatte F. B, Maurice M, Dauwels J, Cichocki A.:
    "Steady-state visually evoked potentials: focus on essential paradigms and future perspectives"
    Progress in Neurobiology, 90(4), 418-438. (2010).
  8. Cichocki A, Phan A-H.:
    "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).
  9. Cichocki A, Shishkin S. L, Musha T, Leonowicz Z, Asada T, Kurachi T.:
    "EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease"
    Clinical Neurophysiology, 116(3), 729-737. (2005).
  10. Amari S, Cichocki A.:
    "Adaptive blind signal processing-neural network approaches"
    Proceedings of the IEEE, 86(10), 2026-2048. (1988).

Lab Members

Principal Investigator

Andrzej Cichocki
Laboratory Head

Core Members

Keiichi Kitajo
Deputy Team Leader
Huy Anh Phan
Research Scientist
Qibin Zhao
Research Scientist
Zbigniew Romuald Struzik
Research Scientist
Zhe Sun
Research Scientist
Huihai Zhao
Research Scientist
Fabien Pierre Robert Lotte
Visiting Researcher
Li Zhu
International Program Associate
Yuya Hirayama
Technical Staff I
Hiroyo Yamaguchi