Multistep Model for Predicting Upper-Limb 3D Isometric Force Application from Pre-Movement Electrocorticographic Features

TL;DR

This study develops a multistep model combining jPCA-RR-HMM, RDA, and LASSO to predict 3D isometric force directions from pre-movement electrocorticography signals, achieving early and accurate movement planning decoding.

Contribution

It introduces a novel multistep modeling approach that improves early prediction of movement direction from ECoG signals, enhancing understanding of motor planning.

Findings

60% true positive force-onset prediction within 250ms

36% accuracy in predicting 3D force direction (above chance)

Direction information detectable up to 400ms before movement onset

Abstract

Neural correlates of movement planning onset and direction may be present in human electrocorticography in the signal dynamics of both motor and non-motor cortical regions. We use a three-stage model of jPCA reduced-rank hidden Markov model (jPCA-RR-HMM), regularized shrunken-centroid discriminant analysis (RDA), and LASSO regression to extract direction-sensitive planning information and movement onset in an upper-limb 3D isometric force task in a human subject. This mode achieves a relatively high true positive force-onset prediction rate of 60% within 250ms, and an above-chance 36% accuracy (17% chance) in predicting one of six planned 3D directions of isometric force using pre-movement signals. We also find direction-distinguishing information up to 400ms before force onset in the pre-movement signals, captured by electrodes placed over the limb-ipsilateral dorsal premotor regions. This approach can contribute to more accurate decoding of higher-level movement goals, at earlier timescales, and inform sensor placement. Our results also contribute to further understanding of the spatiotemporal features of human motor planning.