# 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.

## Key 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.

---
Source: https://tomesphere.com/paper/1702.06251