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Electroencephalography (EEG) activity in the mu frequency band (8-13 Hz) is suppressed during both gesture performance and observation. However, it is not clear if or how particular characteristics within the kinematic execution of gestures map onto dynamic changes in mu activity. Mapping the time course of gesture kinematics onto that of mu activity could help understand which aspects of gestures capture attention and aid in the classification of communicative intent. In this work, we test whether the timing of inflection points within gesture kinematics predicts the occurrence of oscillatory mu activity during passive gesture observation. The timing for salient features of performed gestures in video stimuli was determined by isolating inflection points in the hands' motion trajectories. Participants passively viewed the gesture videos while continuous EEG data was collected. We used wavelet analysis to extract mu oscillations at 11 Hz and at central electrodes and occipital electrodes. We used linear regression to test for associations between the timing of inflection points in motion trajectories and mu oscillations that generalized across gesture stimuli. Separately, we also tested whether inflection point occurrences evoked mu/alpha responses that generalized across participants. Across all gestures and inflection points, and pooled across participants, peaks in 11 Hz EEG waveforms were detected 465 and 535 ms after inflection points at occipital and central electrodes, respectively. A regression model showed that inflection points in the motion trajectories strongly predicted subsequent mu oscillations ([Formula: see text]<0.01); effects were weaker and non-significant for low (17 Hz) and high (21 Hz) beta activity. When segmented by inflection point occurrence rather than stimulus onset and testing participants as a random effect, inflection points evoked mu and beta activity from 308 to 364 ms at central electrodes, and broad activity from 226 to 800 ms at occipital electrodes. The results suggest that inflection points in gesture trajectories elicit coordinated activity in the visual and motor cortices, with prominent activity in the mu/alpha frequency band and extending into the beta frequency band. The time course of activity indicates that visual processing drives subsequent activity in the motor cortex during gesture processing, with a lag of approximately 80 ms.

In order to understand how the central nervous system controls the kinematics of rapid finger and hand movements, we studied the motions of subjects turning a knob to light-emitting diode targets, similar to tuning a radio dial. On many trials, subjects turned the knob with a single, smooth, and regular motion as revealed by the angular position and velocity trajectories, but on others, subjects produced irregularities in the kinematics. Like many past studies, we interpreted these irregularities as discrete corrective submovements. Unlike other studies, we used a direct, objective algorithm to identify overlapping submovements, detecting appreciable inflections in the acceleration traces by examining zero crossings in their derivatives, jerk and snap. The movements without overlapping submovements on average had a near symmetric, bell-shaped velocity profile that was independent of speed, and which matched the theoretical minimum jerk velocity very closely. We proposed three plausible mechanisms for altering the shape of movement kinematics, and implemented a mass-spring model with nonlinear damping to explore the possibilities. Although there was relatively little variability in the shape and symmetry of movements across trials, there was a fair amount of variability in their amplitude. We show that subjects attempted to eliminate the need for corrective submovements by making more accurate primary movements with practice, but that the variability inherent in rapid movements dictated the need for corrective submovements. Subjects used corrective submovements to improve final endpoint accuracy while reducing endpoint variability, resulting in higher task success rates.