Multivariate Analysis of Electrophysiological Signals Reveals the Temporal Properties of Visuomotor Computations for...

The frontoparietal networks underlying grasping movements have been extensively studied, especially using fMRI. Accordingly, whereas much is known about their cortical locus much less is known about the temporal dynamics of visuomotor transformations. Here, we show that multivariate EEG analysis allows for detailed insights into the time course of visual and visuomotor computations of precision grasps. Male and female human participants first previewed one of several objects and, upon its reappearance, reached to grasp it with the thumb and index finger along one of its two symmetry axes. Object shape classifiers reached transient accuracies of 70% at ∼105 ms, especially based on scalp sites over visual cortex, dropping to lower levels thereafter. Grasp orientation classifiers relied on a system of occipital-to-frontal electrodes. Their accuracy rose concurrently with shape classification but ramped up more gradually, and the slope of the classification curve predicted individual reaction times. Further, cross-temporal generalization revealed that dynamic shape representation involved early and late neural generators that reactivated one another. In contrast, grasp computations involved a chain of generators attaining a sustained state about 100 ms before movement onset. Our results reveal the progression of visual and visuomotor representations over the course of planning and executing grasp movements.SIGNIFICANCE STATEMENT Grasping an object requires the brain to perform visual-to-motor transformations of the object's properties. Although much of the neuroanatomic basis of visuomotor transformations has been uncovered, little is known about its time course. Here, we orthogonally manipulated object visual characteristics and grasp orientation, and used multivariate EEG analysis to reveal that visual and visuomotor computations follow similar time courses but display different properties and dynamics.