Title
Capturing beat representations across sound, brain and movement signals: A self-similarity framework
Speaker
Dr. Tomas Lenc from the Rhythm and Brains lab (Institute of Neuroscience, UCLouvain, Belgium)
Forum
Structure & Cognition Cluster meeting on March 6th, 10:45 – 11:45.
Place: RITMO meeting room v217
Zoom: (to follow)
Bio
Tomas Lenc is a postdoctoral research fellow at the Rhythm and Brains lab lab (IONS, UCLouvain, Belgium). During his PhD at the MARCS Institute for Brain, Behaviour and Development (Western Sydney University, Australia; supervisor: Pr. Sylvie Nozaradan; co-superviors: Pr. Peter Keller and Dr. Manuel Varlet) he used electroencephalography (EEG) and behavioral methods to clarify the nature of processes that support perception and sensory-motor synchronization with musical rhythm. Currently, he continues to follow this line of research with the mentorship of Pr. Sylvie Nozaradan, aiming to map the functional network of brain regions involved in musical beat processing using intracerebral EEG in humans. He is generally interested in how the brain makes sense of musical rhythm by transforming rhythmic sensory features into behaviorally-relevant internal categories. Tomas was recently awarded a Marie-Curie postdoctoral fellowship to develop his research independently at the Basque Center on Cognition, Brain and Language (BCBL, Spain) and extend it to speech and language (starting autumn 2024).
Abstract
Capturing beat representations across sound, brain and movement signals: A self-similarity framework
Experiencing music often entails the perception of a periodic beat. Despite being a widespread phenomenon across cultures, the nature and neural underpinnings of beat perception remain largely unknown. In the last decade, there has been a growing interest in developing methods to probe these processes, particularly to measure the extent to which beat-related information is contained in behavioral and neural responses. In this conceptual work, we propose a theoretical framework and practical implementation of an analytic approach to capture beat-related periodicity in real-world signals using frequency-tagging. We highlight its sensitivity in measuring the extent to which the periodicity of a perceived beat is represented in a range of continuous time-varying signals with minimal assumptions. We also discuss a limitation of this approach with respect to its specificity when restricted to measuring beat-related periodicity only from the magnitude spectrum of a signal, and introduce a novel extension of the approach based on autocorrelation to overcome this issue. We test the new autocorrelation-based method using simulated signals and by analyzing empirical datasets, and show how it can be used to process measurements of brain activity as captured with surface EEG in adults and infants as well as intracerebral EEG in human patients. Taken together, the theoretical framework and related methodological advances confirm and elaborate the frequency-tagging approach as a promising window into the processes underlying beat perception and, more generally, temporally coordinated behaviors.