What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Algorithm
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 most cited work include:
- A New Learning Algorithm for Blind Signal Separation (1834 citations)
- Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation (1346 citations)
- Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications (1289 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (45.29%)
- Algorithm (29.42%)
- Pattern recognition (35.10%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (45.29%)
- Pattern recognition (35.10%)
- Electroencephalography (24.52%)
In recent papers he was focusing on the following fields of study:
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.
Between 2016 and 2021, his most popular works were:
- A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update (516 citations)
- A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update (516 citations)
- Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces (123 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
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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