THE BAYESIAN PERIODICITY SPECTRUM TO DISAMBIGUATE OSCILLATORY ACTIVITY
HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales
Presentation
Date TBA
Event Information
Poster Board
PS06-09PM-363
Poster
View posterAbstract
Oscillatory activity in biological signals is commonly evaluated using power spectrum analysis, a technique that is sensitive not only to genuine oscillations but also to broadband and aperiodic signal components. This overlap complicates the interpretation of spectral peaks. Here, we present the periodicity spectrum, an approach based exclusively on phase information that enables a direct and unbiased estimation of oscillatory activity across frequencies, independently of non-oscillatory background activity. In addition, we incorporate a Bayesian framework to formally quantify evidence for the presence or absence of rhythmic components. The method is validated using simulations and applied to multiple types of biological recordings, including subthalamic local field potentials in Parkinson’s disease, accelerometry in essential tremor, and scalp EEG and MEG in healthy participants. Our results show that the Bayesian periodicity spectrum reduces spectral ambiguity, improves frequency resolution, and eliminates frequency bias, providing a more accurate and specific characterization of oscillatory activity in biological signals.
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