A
Andrzej Cichocki
Researcher at Skolkovo Institute of Science and Technology
Publications - 981
Citations - 47609
Andrzej Cichocki is an academic researcher from Skolkovo Institute of Science and Technology. The author has contributed to research in topics: Blind signal separation & Tensor. The author has an hindex of 97, co-authored 952 publications receiving 41471 citations. Previous affiliations of Andrzej Cichocki include University of Warsaw & University of Tokyo.
Papers
More filters
Proceedings Article
A New Learning Algorithm for Blind Signal Separation
TL;DR: A new on-line learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals and has an equivariant property and is easily implemented on a neural network like model.
Book
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
TL;DR: This book provides a broad survey of models and efficient algorithms for Nonnegative Matrix Factorization (NMF), including NMFs various extensions and modifications, especially Nonnegative Tensor Factorizations (NTF) and Nonnegative Tucker Decompositions (NTD).
Book
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Andrzej Cichocki,Shun-ichi Amari +1 more
TL;DR: This volume unifies and extends the theories of adaptive blind signal and image processing and provides practical and efficient algorithms for blind source separation, Independent, Principal, Minor Component Analysis, and Multichannel Blind Deconvolution (MBD) and Equalization.
Book
Adaptive blind signal and image processing
Andrzej Cichocki,Shun-ichi Amari +1 more
TL;DR: Find the secret to improve the quality of life by reading this adaptive blind signal and image processing and make the words as your good value to your life.
Journal ArticleDOI
A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update
Fabien Lotte,Laurent Bougrain,Andrzej Cichocki,Andrzej Cichocki,Maureen Clerc,Marco Congedo,Alain Rakotomamonjy,Florian Yger +7 more
TL;DR: A comprehensive overview of the modern classification algorithms used in EEG-based BCIs is provided, the principles of these methods and guidelines on when and how to use them are presented, and a number of challenges to further advance EEG classification in BCI are identified.