B Chemin1, G Huang2, D Mulders3, A Mouraux3. 1. Institute of NeuroScience (IoNS), System and Cognition Department, Université catholique de Louvain, Belgium; International Laboratory for Brain, Music and Sound Research (BRAMS), Université de Montréal, Canada. Electronic address: baptiste.chemin@uclouvain.be. 2. Institute of NeuroScience (IoNS), System and Cognition Department, Université catholique de Louvain, Belgium; School of Mobile Information Engineering, Sun Yat-Sen University, China. 3. Institute of NeuroScience (IoNS), System and Cognition Department, Université catholique de Louvain, Belgium.
Abstract
BACKGROUND: Many sensorimotor functions are intrinsically rhythmic, and are underlined by neural processes that are functionally distinct from neural responses related to the processing of transient events. EEG frequency tagging is a technique that is increasingly used in neuroscience to study these processes. It relies on the fact that perceiving and/or producing rhythms generates periodic neural activity that translates into periodic variations of the EEG signal. In the EEG spectrum, those variations appear as peaks localized at the frequency of the rhythm and its harmonics. NEW METHOD: Many natural rhythms, such as music or dance, are not strictly periodic and, instead, show fluctuations of their period over time. Here, we introduce a time-warping method to identify non-strictly-periodic EEG activities in the frequency domain. RESULTS: EEG time-warping can be used to characterize the sensorimotor activity related to the performance of self-paced rhythmic finger movements. Furthermore, the EEG time-warping method can disentangle auditory- and movement-related EEG activity produced when participants perform rhythmic movements synchronized to an acoustic rhythm. This is possible because the movement-related activity has different period fluctuations than the auditory-related activity. COMPARISON WITH EXISTING METHODS: With the classic frequency-tagging approach, rhythm fluctuations result in a spreading of the peaks to neighboring frequencies, to the point that they cannot be distinguished from background noise. CONCLUSIONS: The proposed time-warping procedure is as a simple and effective mean to study natural non-strictly-periodic rhythmic neural processes such as rhythmic movement production, acoustic rhythm perception and sensorimotor synchronization.
BACKGROUND: Many sensorimotor functions are intrinsically rhythmic, and are underlined by neural processes that are functionally distinct from neural responses related to the processing of transient events. EEG frequency tagging is a technique that is increasingly used in neuroscience to study these processes. It relies on the fact that perceiving and/or producing rhythms generates periodic neural activity that translates into periodic variations of the EEG signal. In the EEG spectrum, those variations appear as peaks localized at the frequency of the rhythm and its harmonics. NEW METHOD: Many natural rhythms, such as music or dance, are not strictly periodic and, instead, show fluctuations of their period over time. Here, we introduce a time-warping method to identify non-strictly-periodic EEG activities in the frequency domain. RESULTS: EEG time-warping can be used to characterize the sensorimotor activity related to the performance of self-paced rhythmic finger movements. Furthermore, the EEG time-warping method can disentangle auditory- and movement-related EEG activity produced when participants perform rhythmic movements synchronized to an acoustic rhythm. This is possible because the movement-related activity has different period fluctuations than the auditory-related activity. COMPARISON WITH EXISTING METHODS: With the classic frequency-tagging approach, rhythm fluctuations result in a spreading of the peaks to neighboring frequencies, to the point that they cannot be distinguished from background noise. CONCLUSIONS: The proposed time-warping procedure is as a simple and effective mean to study natural non-strictly-periodic rhythmic neural processes such as rhythmic movement production, acoustic rhythm perception and sensorimotor synchronization.
Authors: Dounia Mulders; Cyril de Bodt; Nicolas Lejeune; Arthur Courtin; Giulia Liberati; Michel Verleysen; André Mouraux Journal: PLoS One Date: 2020-04-23 Impact factor: 3.240