- What is Independent component analysis algorithm?
- Why Independent component analysis is used?
- Is ICA supervised or unsupervised?
What is Independent component analysis algorithm?
Independent component analysis (ICA) is known as a blind-source separation technique. It attempts to extract underlying signals that, when combined, produce the resulting EEG. It operates on the assumption that there are underlying signals that are linearly mixed to produce the EEG.
Why Independent component analysis is used?
It is also used for signals that are not supposed to be generated by mixing for analysis purposes. A simple application of ICA is the "cocktail party problem", where the underlying speech signals are separated from a sample data consisting of people talking simultaneously in a room.
Is ICA supervised or unsupervised?
Since ICA is an unsupervised learning, extracted independent components are not always useful for recognition purposes.