AUTHOR=Kostoglou Kyriaki , Müller-Putz Gernot R. TITLE=Using linear parameter varying autoregressive models to measure cross frequency couplings in EEG signals JOURNAL=Frontiers in Human Neuroscience VOLUME=16 YEAR=2022 URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2022.915815 DOI=10.3389/fnhum.2022.915815 ISSN=1662-5161 ABSTRACT=

For years now, phase-amplitude cross frequency coupling (CFC) has been observed across multiple brain regions under different physiological and pathological conditions. It has been suggested that CFC serves as a mechanism that facilitates communication and information transfer between local and spatially separated neuronal populations. In non-invasive brain computer interfaces (BCI), CFC has not been thoroughly explored. In this work, we propose a CFC estimation method based on Linear Parameter Varying Autoregressive (LPV-AR) models and we assess its performance using both synthetic data and electroencephalographic (EEG) data recorded during attempted arm/hand movements of spinal cord injured (SCI) participants. Our results corroborate the potentiality of CFC as a feature for movement attempt decoding and provide evidence of the superiority of our proposed CFC estimation approach compared to other commonly used techniques.