Abstract
Although efforts to characterize human movement through electroencephalography (EEG) have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such movements. In this study we used EEG and inertial sensors to record brain activity and movement of five skilled and certified Laban Movement Analysis (LMA) dancers. Each dancer performed whole body movements of three Action types: movements devoid of expressive qualities (“Neutral”), non-expressive movements while thinking about specific expressive qualities (“Think”), and enacted expressive movements (“Do”). The expressive movement qualities that were used in the “Think” and “Do” actions consisted of a sequence of eight Laban Effort qualities as defined by LMA—a notation system and language for describing, visualizing, interpreting and documenting all varieties of human movement. We used delta band (0.2–4 Hz) EEG as input to a machine learning algorithm that computed locality-preserving Fisher's discriminant analysis (LFDA) for dimensionality reduction followed by Gaussian mixture models (GMMs) to decode the type of Action. We also trained our LFDA-GMM models to classify all the possible combinations of Action Type and Laban Effort quality (giving a total of 17 classes). Classification accuracy rates were 59.4 ± 0.6% for Action Type and 88.2 ± 0.7% for Laban Effort quality Type. Ancillary analyses of the potential relations between the EEG and movement kinematics of the dancer's body, indicated that motion-related artifacts did not significantly influence our classification results. In summary, this research demonstrates that EEG has valuable information about the expressive qualities of movement. These results may have applications for advancing the understanding of the neural basis of expressive movements and for the development of neuroprosthetics to restore movements.
Highlights
During “neutral” action, subjects were directed to perform functional movements without any additional qualities of expression. This was followed by the “think” condition where subjects continued to perform functional movements, but imagined a particular Laban Effort quality instructed by the experimenter
As we were interested in inferring expressive qualities, all the “neutral” instances, which were devoid of willed expressiveness, were collapsed within a superset “neutral” leaving a total of 17 distinct classes of expressive movements to infer from scalp EEG (“neutral” + “think” × 8 efforts + “do” × 8 efforts)
CLASSIFICATION OF EXPRESSIVE MOVEMENTS FROM SCALP EEG In this study we demonstrate the feasibility of classifying expressive movement from delta band, EEG signals
Summary
Neural engineering approaches to understanding the neural basis of human movement using scalp electroencephalography (EEG) have uncovered dynamic cortical contributions to the initiation and control of human lower limb movements such as cycling (Jain et al, 2013); treadmill walking (Gwin et al, 2010, 2011; Presacco et al, 2011, 2012; Cheron et al, 2012; Petersen et al, 2012; Severens et al, 2012; Schneider et al, 2013), and even robotic assisted gait (Wagner et al, 2012; Kilicarslan et al, 2013). Most of these studies have been limited to slow walking speeds and have been constrained by treadmills or the cycling or robotic devices used in the tasks, and have yet to examine more natural, and less constrained, expressive movements To address this important limitations, a mobile EEG-based brain imaging (MoBI) approach may be a valuable tool for recording and analyzing what the brain and the body do during the production of expressive movements, what the brain and the body experience, and what or how the brain self-organizes while movements of physical virtuosity are modified by expressive qualities that communicate emotional tone and texture—the basic language of human interactions. Studies of the so-called human action observation network, comprised of ventral premotor cortex, inferior parietal lobe, and the superior temporal sulcus, have shown dissociable neural substrates for body motion and physical experience during the observation of dance (Cross et al, 2006, 2009). Orgs et al (2008) reported modulation of event-related desynchronization (ERD) in alpha and beta bands between 7.5 and 25 Hz in Frontiers in Human Neuroscience www.frontiersin.org
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