Multimodal mobile brain and body imaging for quantification of dance motor sequence learning

Understanding motor learning in naturalistic settings presents a key challenge in neuroscience. While paradigms like the Discrete Sequence Production (DSP) task have advanced our knowledge, investigating more naturalistic tasks like dance with multi-limbed coordination can help further advance the understanding of complex mechanisms. It can advance motor learning by providing more profound insights into coordination dynamics, movement execution, balance, and decision-making. We have developed a modified DSP methodology that replaces keyboard pressing with dance-stepping, allowing simultaneous electroencephalography (EEG), behavioral, and kinematic recordings to quantify neurophysiological and motor dynamics. Using an E-Prime ^Ⓡ script in a go/no-go approach, our method accommodates both a setup with minimal hardware and also a scalable approach with markerless motion capture and mobile EEG for neuroimaging. By leveraging Mobile Brain and Body Imaging (MOBI), we enhance the investigation of neuro-mechanisms underlying motor learning. We also discuss future directions and accessibility, including a publicly available video of the experimental procedure (https://youtu.be/zFP1rWJ2FJ8?si=DJ8q7fbfhltSLehz), enabling broader replication and application of our methodology.