EEG time-warping to study non-strictly-periodic EEG signals related to the production of rhythmic movements

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.

Gan Huang

My research interests include Neural Modulation, Brain Computer Interface and Neural Prosthetics.