Table 1.

A list of methods, their abbreviations and short descriptions.

Method	Abbreviation	Description
Principal component analysis	PCA	It finds linearly uncorrelated components in a given dataset. The successive components explain a decreasing amount of variance.
Independent component analysis	ICA	It finds statistically independent components in a given dataset and removes noise and separates artifacts.
Non-negative matrix factorization	NMF	It finds a parts-based representation with each component accounting for a particular segment of the data space.
Cosine series	—	It is a pre-defined set of components. It is used to obtain projected amplitudes like components from the other methods.
jPCA	—	It uses components defined by PCA and reorients them so that the projected amplitudes show a strong oscillation over time.