His main research concerns Artificial intelligence, Algorithm, Pattern recognition, Blind signal separation and Brain–computer interface. His studies in Artificial intelligence integrate themes in fields like Machine learning and Computer vision. His work deals with themes such as Matrix decomposition, Non-negative matrix factorization, Independent component analysis, Mathematical optimization and Signal processing, which intersect with Algorithm.
His work carried out in the field of Pattern recognition brings together such families of science as Spatial filter, Robustness and Nonlinear system. His Blind signal separation research includes elements of Matrix, Sparse matrix, Source separation, Component analysis and Sparse approximation. His Brain–computer interface study incorporates themes from Speech recognition and Event-related potential.
His primary areas of study are Artificial intelligence, Algorithm, Pattern recognition, Electroencephalography and Speech recognition. His research investigates the connection between Artificial intelligence and topics such as Brain–computer interface that intersect with issues in Stimulus. His Algorithm research is multidisciplinary, relying on both Matrix decomposition, Matrix, Non-negative matrix factorization, Blind signal separation and Mathematical optimization.
His Blind signal separation research incorporates elements of Independent component analysis, Sparse approximation, Source separation and Signal processing. His Pattern recognition study frequently links to related topics such as Tensor. Electroencephalography is the subject of his research, which falls under Neuroscience.
Andrzej Cichocki mainly investigates Artificial intelligence, Pattern recognition, Electroencephalography, Brain–computer interface and Feature extraction. Andrzej Cichocki has included themes like Machine learning and Interface in his Artificial intelligence study. His Pattern recognition research is multidisciplinary, incorporating elements of Statistical hypothesis testing, Noise reduction, Tensor and Feature.
His Brain–computer interface research incorporates themes from Stimulus, Speech recognition, Information transfer and Human–computer interaction. His Artificial neural network study combines topics in areas such as Algorithm, Compression and Rank. The Algorithm study combines topics in areas such as Matrix and Multilinear map.
Andrzej Cichocki mainly focuses on Artificial intelligence, Pattern recognition, Brain–computer interface, Motor imagery and Electroencephalography. The study incorporates disciplines such as Machine learning and Interface in addition to Artificial intelligence. The Pattern recognition study combines topics in areas such as Optimization problem, Theoretical computer science and Spectral bands.
His Brain–computer interface research includes themes of Classifier, Speech recognition, Extreme learning machine, Kernel and Polynomial kernel. His biological study spans a wide range of topics, including Mental representation, Correlation, Communication channel, Spatial filter and Algorithm. His Electroencephalography research incorporates elements of Artificial neural network, Visualization, Cognition and Synchronization.
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Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Andrzej Cichocki;Shun-ichi Amari.
A New Learning Algorithm for Blind Signal Separation
Shun-ichi Amari;Andrzej Cichocki;Howard Hua Yang.
neural information processing systems (1995)
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
Andrzej Cichocki;Rafal Zdunek;Anh Huy Phan;Shun-ichi Amari.
Adaptive blind signal and image processing
Andrzej Cichocki;Shun-ichi Amari.
Nonnegative Matrix and Tensor Factorizations
Andrzej Cichocki;Rafal Zdunek;Anh Huy Phan;Shun-Ichi Amari.
IEEE Signal Processing Magazine (2009)
Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis
Andrzej Cichocki;Danilo Mandic;Lieven De Lathauwer;Guoxu Zhou.
IEEE Signal Processing Magazine (2015)
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.
Journal of Neural Engineering (2018)
Steady-state visually evoked potentials: focus on essential paradigms and future perspectives.
François-Benoît Vialatte;Monique Maurice;Justin Dauwels;Andrzej Cichocki.
Progress in Neurobiology (2010)
InfoSleuth: agent-based semantic integration of information in open and dynamic environments
R. J. Bayardo;W. Bohrer;R. Brice;A. Cichocki.
international conference on management of data (1997)
Adaptive blind signal processing-neural network approaches
S. Amari;A. Cichocki.
Proceedings of the IEEE (1998)
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