Electrocorticographic Dynamics Predict Visually Guided Motor Imagery of Grasp Shaping

TL;DR

This study demonstrates that neural dynamics during visually-guided imagined grasp shaping can decode intended movement type and phase with high accuracy, advancing neuroprosthetic control and understanding of motor imagery.

Contribution

The paper introduces a novel approach combining Procrustes analysis and LASSO regression to decode grasp shaping stages and movement goals from neural data without prior BCI training.

Findings

Achieved 72% accuracy in movement type classification

Predicted grasp stage with R2=0.4

Neural trajectory structures are consistent within individuals

Abstract

Identification of intended movement type and movement phase of hand grasp shaping are critical features for the control of volitional neuroprosthetics. We demonstrate that neural dynamics during visually-guided imagined grasp shaping can encode intended movement. We apply Procrustes analysis and LASSO regression to achieve 72% accuracy (chance = 25%) in distinguishing between visually-guided imagined grasp trajectories. Further, we can predict the stage of grasp shaping in the form of elapsed time from start of trial (R2=0.4). Our approach contributes to more accurate single-trial decoding of higher-level movement goals and the phase of grasping movements in individuals not trained with brain-computer interfaces. We also find that the overall time-varying trajectory structure of imagined movements tend to be consistent within individuals, and that transient trajectory deviations within trials return to the task-dependent trajectory mean. These overall findings may contribute to the further understanding of the cortical dynamics of human motor imagery.