Cross-frequency bispectral EEG analysis of reach-to-grasp planning and execution

Neural motor control of reach-to-grasp emerges from complex, nonlinear interactions across multiple brain cortices. However, most electroencephalography (EEG)-based motor analysis has largely relied on linear and second-order spectral measures. Here, we investigate whether higher-order cross-frequency dynamics encode meaningful distinctions between motor planning and execution during natural reach-to-grasp movements. Using a cue-based experimental paradigm, EEG was recorded during precision and power plan-to-grasp tasks, enabling stage-resolved analysis of grasp planning and execution-related neural activity. Cross-frequency bispectral analysis was applied to compute complex 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 revealed that execution is associated with stronger nonlinear coupling than planning, with dominant contributions from β- and γ-driven interactions. In contrast, decoding of precision versus power grasps showed similar performance across stages, indicating that grasp-type representations emerge during planning and persist into execution. Exploratory single-feature analyses further identified focal, stage-dependent modulation of nonlinear coupling in central motor regions. Informative bispectral features reflected coordinated activity across prefrontal, central, and occipital areas, while feature redundancy enabled dimensionality reduction without loss of performance. Compared with the conventional analytical methods as baselines, bispectral features provided consistent advantages for grasp-type discrimination and multiclass classification, highlighting the value of nonlinear cross-frequency analysis. In summary, our results extend bispectral analysis to clinically relevant grasping tasks and highlight nonlinear cross-frequency coupling as an informative marker of motor stages offering a foundation for future BCI and neuroprosthetic research.