Robust EEG Functional Connectivity Metrics for Decoding Action Observation Conditions and Observed Actions

Abstract

Action observation (AO) paradigms probe motor-system engagement, yet the electroencephalographic (EEG) functional connectivity (FC) metrics that best capture AO-related dynamics remain unclear. This pilot study benchmarked five sensor-level FC metrics, including coherence (COH), imaginary coherence (iCOH), phase-locking value (PLV), partial directed coherence (PDC), and spectral Granger causality (SpcG), for decoding AO stimuli in five healthy adults. EEG signals were recorded while participants observed upper-limb actions performed by human or robot agents, as well as non-action control stimuli. Ten motor-area channels were analyzed in the alpha (8-12 Hz) and beta (13-30 Hz) bands. Trial-wise 10 x 10 FC matrices were used as inputs to multiple classifiers for two tasks: (i) six-class AO-condition decoding and (ii) five-class action-type decoding. Across both tasks, metrics robust to volume conduction consistently outperformed their counterparts. iCOH achieved the highest macro-area under the receiver operating characteristic curve (macro-AUC) for most classifiers, with PDC and SpcG showing comparable performance. Graph neural networks (GNNs) provided the most robust and stable results across all FC metrics, while convolutional neural networks and random forests also performed strongly. These findings highlight the importance of suppressing zero-phase-lag interactions and incorporating directed connectivity when characterizing AO-related brain activity. They further demonstrate the ability of GNNs to exploit the inherent graph structure of FC representations, providing practical guidance for selecting connectivity measures and machine learning models in future large-scale studies of action observation.