Laboratory for Advanced Brain Signal Processing
Laboratory Head
Andrzej CICHOCKI (Ph.D.)
The laboratory for Advanced Brain Signal Processing is focused on developing novel and state of the art methods to extract, detect, recognize, find functional connectivity, and classify brain signals and to gain the insights building intelligent feature extraction systems. Central research interest: * Multi-modal multi-sensory multidimensional brain data analysis , especially EEG/MEG, fMRI data (Human Perception: sound, vision, odors, taste, tactile), * Bio-inspired signal processing - Blind Sources Separation: (Sparse Component Analysis-SCA, Independent Component Analysis-ICA, Morphological Component Analysis -MCA, Nonnegative Matrix Factorization-NMF, Nonnegative Tensor Factorization-NTF, Time-Frequency Morohological Component Analyzer-TFCA), * Brain Computer Interface (BCI) / Human Computer Interaction (HCI). Development and investigation of models, architectures (structures) and associated learning algorithms of artificial neural systems. We develop novel reconstruction algorithms and implementations for bio-medical imaging applications that can greatly enhance our ability to monitor neuroimage structures and processes. The imaging systems of our interests include existing modalities such as EEG, fMRI, and NIRS as well as emerging technologies.
- Blind signal processing (BSP) and blind source separation algorithms for biological signals, especially EEG and NIRS
- Time-Frequency Representation (TFR), Multiway data analysis, tensor decompositions
- Developments of software/hardware for Human/Brain Computer Interface(H/BCI).
- Models, architectures and learning algorithms of artificial neural network systems.
- Early detection and classification of dementia, especially Alzheimer Disease (AD).
- Cichocki, A., Shishkin, S., Musha, T., Leonowicz, Z., Asada, T., and Kurachi, T:
"EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease"
Clinical Neurophysiology, 116, (2005). - Y. Li, S. Amari, A. Cichocki, D. W. C. Ho, and S. Xie,:
"Underdetermined Blind Source Separation Based on Sparse Representation"
IEEE Transactions On Signal Processing, Vol. 54, No. 2, February 2006, pp 423-437. - Li. Y., Cichocki, A., and Amari, S.:
"Analysis of sparse representation and blind source separation"
Neural Computation, 16, 6, 1193-1234 (2004). - Cichocki, A., and Amari, S.:
"Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. (550 pages)"
monograph Wiley (2003). - A. Cichocki, Y. Washizawa, T. Rutkowski, H. Bakardjian, A.-H. Phan, S. Choi, H. Lee, Q. Zhao, L. Zhang, and Y. Li,:
"Noninvasive BCIs: Multiway signal-processing array decompositions"
IEEE Computer, vol. 41, no. 10, pp. 34-42, 2008 (invited paper) - F. Vialatte, M Maurice-Vialtte,J. Dauwels and A. Cichocki.:
"Steady-State Visually Evoked Potentials: Focus on Essential Paradigms and Future Perspectives."
Progress in Neurobiology 90(4), 418-438 (2010). - C. Caiafa and A. Cichocki:
"Generalizing the Column-row Matrix Decomposition to Multi-way Arrays."
Linear Algebra and its Applications, 433 (3), 557-573 (2010). - A. Cichocki and S. Amari,:
"Families of Alpha-Beta-and Gamma-Divergences: Flexible and Robust Measures of Similarities."
Entropy, 12, 1532-1568, (2010) - A. Cichocki and A-H. Phan:
"Fast local algorithms for large scale Nonnegative Matrix and Tensor Factorizations"
IEICE Transaction on Fundamentals, E92-A(3), pp.708-721, March 2009 (invited paper) . - A. Cichocki, R. Zdunek, A-H Phan and S. Amari:
"Nonnegative Matrix and Tensor Factorizations"
John Wiley, New York, (477 pages) September 2009.