Laboratory for Advanced Brain Signal Processing

The laboratory for Advanced Brain Signal Processing is focused on developing novel and state of the art methods to extract, detect, cluster, recognize, find functional connectivity, and classify brain signals and neurimages to gain the insights building intelligent feature extraction systems. Central research interest: * Multi-modal multi-sensory multidimensional brain data analysis , especially EEG/MEG, fMRI, sMRI, data (Human Perception: sound, music, vision, 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 and Sparse Tensor Factorization-NTF, Time-Frequency Morohological Component Analyzer-TFCA), * Brain Computer Interface (BCI) / Human Robot Interactions (HRI). 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, classify and cluster neuroimages. Early detection of mental diseases: AD, ADHD, ASD

Research Subjects

• 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 mental deseases, especially Alzheimer Disease (AD), ADHD, ASD.

Publications

1. C Caiafa and A Cichocki:
   "Computing Sparse Representations of Multidimensional Signals Using Kronecker Bases."
   Neural Computation, 25 (1), 186-220, (2013).
2. Q Zhao, C Caiafa, D Mandic, Z Chao, Y Nagasaka, N Fujii, L Zhang, and 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).
3. A. Cichocki, S. Cruces and S. Amari:
   "Generalized Alpha-Beta Divergences and their Application to Robust Nonnegative Matrix Factorization"
   Entropy, 13(12), 134-170, (2011).
4. 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).
5. J Dauwels, F Vialatte, T Musha, A Cichocki:
   "A Comparative Study of Synchrony Measures for Early Diagnosis of Alzheimer’s Disease Based on EEG.."
   NeuroImage, 49 (1), 668-693, (2010)
6. A. Cichocki, R. Zdunek, A-H Phan and S. Amari:
   "Nonnegative Matrix and Tensor Factorizations (Monograph -650 pages)"
   John Wiley, New York, (477 pages) September 2009.
7. 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)
8. 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).
9. Li. Y., Cichocki, A., and Amari, S.:
   "Analysis of Sparse Representation and Blind Source Separation"
   Neural Computation, 16, 6, 1193-1234 (2004).
10. Cichocki, A., and Amari, S.:
    "Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. (550 pages) "
    monograph Wiley (2003).