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Article

Cross-Frequency Power-Power Coupling Analysis: A Useful Cross-Frequency Measure to Classify ICA-Decomposed EEG

1
Human Media Interaction, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7522 NB Enschede, The Netherlands
2
Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(24), 7040; https://doi.org/10.3390/s20247040
Received: 26 October 2020 / Revised: 28 November 2020 / Accepted: 3 December 2020 / Published: 9 December 2020
Magneto-/Electro-encephalography (M/EEG) commonly uses (fast) Fourier transformation to compute power spectral density (PSD). However, the resulting PSD plot lacks temporal information, making interpretation sometimes equivocal. For example, consider two different PSDs: a central parietal EEG PSD with twin peaks at 10 Hz and 20 Hz and a central parietal PSD with twin peaks at 10 Hz and 50 Hz. We can assume the first PSD shows a mu rhythm and the second harmonic; however, the latter PSD likely shows an alpha peak and an independent line noise. Without prior knowledge, however, the PSD alone cannot distinguish between the two cases. To address this limitation of PSD, we propose using cross-frequency power–power coupling (PPC) as a post-processing of independent component (IC) analysis (ICA) to distinguish brain components from muscle and environmental artifact sources. We conclude that post-ICA PPC analysis could serve as a new data-driven EEG classifier in M/EEG studies. For the reader’s convenience, we offer a brief literature overview on the disparate use of PPC. The proposed cross-frequency power–power coupling analysis toolbox (PowPowCAT) is a free, open-source toolbox, which works as an EEGLAB extension. View Full-Text
Keywords: EEG; MEG; fourier transform; cross-frequency coupling; comodulogram; comodugram; independent component analysis EEG; MEG; fourier transform; cross-frequency coupling; comodulogram; comodugram; independent component analysis
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MDPI and ACS Style

Thammasan, N.; Miyakoshi, M. Cross-Frequency Power-Power Coupling Analysis: A Useful Cross-Frequency Measure to Classify ICA-Decomposed EEG. Sensors 2020, 20, 7040. https://doi.org/10.3390/s20247040

AMA Style

Thammasan N, Miyakoshi M. Cross-Frequency Power-Power Coupling Analysis: A Useful Cross-Frequency Measure to Classify ICA-Decomposed EEG. Sensors. 2020; 20(24):7040. https://doi.org/10.3390/s20247040

Chicago/Turabian Style

Thammasan, Nattapong, and Makoto Miyakoshi. 2020. "Cross-Frequency Power-Power Coupling Analysis: A Useful Cross-Frequency Measure to Classify ICA-Decomposed EEG" Sensors 20, no. 24: 7040. https://doi.org/10.3390/s20247040

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