Cross-Frequency Bispectral EEG Analysis of Reach-to-Grasp Planning and Execution

TL;DR

This study demonstrates that nonlinear cross-frequency EEG interactions effectively distinguish between motor planning and execution phases in natural reach-to-grasp tasks, highlighting their potential for brain-computer interfaces.

Contribution

It introduces the use of bispectral analysis to capture nonlinear neural dynamics during real-world motor behavior, advancing EEG decoding methods.

Findings

Execution shows stronger nonlinear coupling than planning.

Grasp-type decoding is consistent across planning and execution.

Informative features involve prefrontal, central, and occipital regions.

Abstract

Neural control of grasping arises from nonlinear interactions across multiple brain rhythms, yet EEG-based motor decoding has largely relied on linear, second-order spectral features. Here, we examine whether higher-order cross-frequency dynamics distinguish motor planning from execution during natural reach-to-grasp behavior. EEG was recorded in a cue-based paradigm during executed precision and power grips, enabling stage-resolved analysis of preparatory and execution-related neural activity. Cross-frequency bispectral analysis was used to compute bicoherence matrices across canonical frequency band pairs, from which magnitude- and phase-based features were extracted. Classification, permutation-based feature selection, and within-subject statistical testing showed that execution is characterized by substantially stronger and more discriminative nonlinear coupling than planning, with dominant contributions from beta- and gamma-driven interactions. In contrast, decoding of precision versus power grips achieved comparable performance during planning and execution, indicating that grasp-type representations emerge during planning and persist into execution. Spatial and spectral analyses further revealed that informative bispectral features reflect coordinated activity across prefrontal, central, and occipital regions. Despite substantial feature redundancy, effective dimensionality reduction preserved decoding performance. Together, these findings demonstrate that nonlinear cross-frequency coupling provides an interpretable and robust marker of motor planning and execution, extending bispectral EEG analysis to ecologically valid grasping and supporting its relevance for brain-computer interfaces and neuroprosthetic control.