Macroscopic EEG reveals discriminative low-frequency oscillations in plan-to-grasp visuomotor tasks

OBJECTIVE: 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. METHODS: We introduce a 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. RESULTS: 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. CONCLUSION: This work demonstrated the role of low-frequency oscillations in decoding grasp type during planning using noninvasive EEG. SIGNIFICANCE: These findings provide a foundation toward scalable, intention-driven Brain-Machine-Interface (BMI) control strategies.