Estimation of narrowband amplitude and phase from electrophysiology signals for phase-amplitude coupling studies: a comparison of methods

Many functional connectivity studies based on electrophysiological measurements, such as electro- and magnetoencephalography (EEG/MEG), start their investigations by extracting a narrowband representation of brain activity time series, and then computing their envelope amplitudes and instantaneous phases, which serve as inputs to subsequent data processing. The two most popular approaches for obtaining these narrowband amplitudes and phases are: bandpass filtering followed by Hilbert transform (we call this the Hilbert approach); and convolution with wavelet kernels (the wavelet approach). In this work, we investigate how these two approaches perform in detecting the phenomenon of phase-amplitude coupling (PAC), whereby the amplitude of a high-frequency signal is driven by the phase of a low-frequency signal. The comparison of both approaches is carried out by means of simulated brain activity, from which we run receiver operating characteristic (ROC) analyses, and of experimental MEG data from a visuomotor coordination study. The ROC analyses show that the classification accuracy of the Hilbert approach is markedly better than that of the wavelet approach in most scenarios tested. As for the visuomotor data, the use of the Hilbert approach allows the identification of effects to which the wavelet approach is insensitive, such as widespread areas in the brain with significant task-based changes in PAC between the delta (2-5 Hz) and the gamma (30-90 Hz) frequency bands. These results provide strong evidence that the Hilbert approach yields better performance, at least in the context of PAC estimates, and should be preferred over the wavelet approach.