In 2013, I had the opportunity to be co-lead author for a study about classifying neural activity based on expressive movement performed by expert Laban Movement Analysts (LMA) and performers. This work required the translation of the Laban Movement Elements into an engineering framework to be integrated as labels into a machine learning algorithm. Our group worked closely with dance professors and LMA experts to carry out the study.

An efficient translation and understanding between concepts between the two disciplines was essential to identify and analyze the broad impact of the study. Using non-invasive active scalp EEG, we aimed to extract the neural information that modulated expressive movements performed during dance. The study emerged from interdisciplinary questions about the differences in neural engagement between functional and expressive movement in elite performers of movement; specifically, dance and movement theater.

Dance has been studied primarily as elite athletic movement. And yet, dancers train for years to express nuanced and complex qualities in order to tell a story, express an emotion, or locate a situation. No previous data had been found between these two modalities of movement; thus the study was nascent.