Macroscopic EEG Reveals Discriminative Low-Frequency Oscillations in Plan-to-Grasp Visuomotor Tasks

TL;DR

This study uses EEG to identify neural oscillation patterns associated with different grasp types during visuomotor tasks, revealing low-frequency oscillations as key indicators for grasp planning and execution.

Contribution

It introduces a novel EEG-based platform and analysis method to distinguish grasp types, highlighting the role of low-frequency oscillations in visuomotor planning and execution.

Findings

Low-frequency oscillations (0.5-8 Hz) reliably distinguish grasp types during planning and execution.

Support vector machine classification achieved over 75% accuracy in discriminating grasp types.

Discriminative features are localized in frontoparietal and motor networks.

Abstract

The vision-based grasping brain network integrates visual perception with cognitive and motor processes for visuomotor tasks. While invasive recordings have successfully decoded localized neural activity related to grasp type planning and execution, macroscopic neural activation patterns captured by noninvasive electroencephalography (EEG) remain far less understood. We introduce a novel vision-based grasping platform to investigate grasp-type-specific (precision, power, no-grasp) neural activity across large-scale brain networks using EEG neuroimaging. The platform isolates grasp-specific planning from its associated execution phases in naturalistic visuomotor tasks, where the Filter-Bank Common Spatial Pattern (FBCSP) technique was designed to extract discriminative frequency-specific features within each phase. Support vector machine (SVM) classification discriminated binary (precision vs. power, grasp vs. no-grasp) and multiclass (precision vs. power vs. no-grasp) scenarios for each phase, and were compared against traditional Movement-Related Cortical Potential (MRCP) methods. Low-frequency oscillations (0.5-8 Hz) carry grasp-related information established during planning and maintained throughout execution, with consistent classification performance across both phases (75.3-77.8\%) for precision vs. power discrimination, compared to 61.1\% using MRCP. Higher-frequency activity (12-40 Hz) showed phase-dependent results with 93.3\% accuracy for grasp vs. no-grasp classification but 61.2\% for precision vs. power discrimination. Feature importance using SVM coefficients identified discriminative features within frontoparietal networks during planning and motor networks during execution. This work demonstrated the role of low-frequency oscillations in decoding grasp type during planning using noninvasive EEG.