Recent evidence has revealed cross-frequency coupling and, particularly, phase-amplitude coupling (PAC) as an important strategy for the brain to accomplish a variety of high-level cognitive and sensory functions. However, decoding PAC is still challenging. This contribution presents REPAC, a reliable and robust algorithm for modeling and detecting PAC events in EEG signals. First, we explain the synthesis of PAC-like EEG signals, with special attention to the most critical parameters that characterize PAC, i.e., SNR, modulation index, duration of coupling. Second, REPAC is introduced in detail. We use computer simulations to generate a set of random PAC-like EEG signals and test the performance of REPAC with regard to a baseline method. REPAC is shown to outperform the baseline method even with realistic values of SNR, e.g., −10 dB. They both reach accuracy levels around 99%, but REPAC leads to a significant improvement of sensitivity, from 20.11% to 65.21%, with comparable specificity (around 99%). REPAC is also applied to a real EEG signal showing preliminary encouraging results.

Cisotto, G. (2021). REPAC: Reliable estimation of phase-amplitude coupling in brain networks. In 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) (pp.1075-1079). 345 E 47TH ST, NEW YORK, NY 10017 USA : IEEE [10.1109/ICASSP39728.2021.9414749].

REPAC: Reliable estimation of phase-amplitude coupling in brain networks

Cisotto, G.
Primo
2021

Abstract

Recent evidence has revealed cross-frequency coupling and, particularly, phase-amplitude coupling (PAC) as an important strategy for the brain to accomplish a variety of high-level cognitive and sensory functions. However, decoding PAC is still challenging. This contribution presents REPAC, a reliable and robust algorithm for modeling and detecting PAC events in EEG signals. First, we explain the synthesis of PAC-like EEG signals, with special attention to the most critical parameters that characterize PAC, i.e., SNR, modulation index, duration of coupling. Second, REPAC is introduced in detail. We use computer simulations to generate a set of random PAC-like EEG signals and test the performance of REPAC with regard to a baseline method. REPAC is shown to outperform the baseline method even with realistic values of SNR, e.g., −10 dB. They both reach accuracy levels around 99%, but REPAC leads to a significant improvement of sensitivity, from 20.11% to 65.21%, with comparable specificity (around 99%). REPAC is also applied to a real EEG signal showing preliminary encouraging results.
No
paper
Scientifica
Brain networks; EEG; Modulation; Phase-amplitude coupling; REPAC;
English
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - JUN 06-11, 2021
978-1-7281-7605-5
Cisotto, G. (2021). REPAC: Reliable estimation of phase-amplitude coupling in brain networks. In 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) (pp.1075-1079). 345 E 47TH ST, NEW YORK, NY 10017 USA : IEEE [10.1109/ICASSP39728.2021.9414749].
Cisotto, G
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/366715
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