Parallel Factor Analysis as an exploratory tool for wavelet transformed event-related EEG.

@article{Mrup2006ParallelFA,
  title={Parallel Factor Analysis as an exploratory tool for wavelet transformed event-related EEG.},
  author={Morten M\orup and Lars Kai Hansen and Christoph Siegfried Herrmann and Josef Parnas and Sidse Marie Arnfred},
  journal={NeuroImage},
  year={2006},
  volume={29 3},
  pages={938-47}
}
In the decomposition of multi-channel EEG signals, principal component analysis (PCA) and independent component analysis (ICA) have widely been used. However, as both methods are based on handling two-way data, i.e. two-dimensional matrices, multi-way methods might improve the interpretation of frequency transformed multi-channel EEG of channel x frequency x time data. The multi-way decomposition method Parallel Factor (PARAFAC), also named Canonical Decomposition (CANDECOMP), was recently used… CONTINUE READING

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