Evaluation of Human Posterior Parietal Cortex in Gesture Decoding Performance Enhancement Using Stereoelectroencephalography (SEEG) Signals

Previous intracranial electroencephalography (iEEG)-based brain-machine interfaces (BMIs) towards gesture decoding mostly used neural signals from the primary sensorimotor cortex while largely ignoring the hand movement related signals from posterior parietal cortex (PPC). In this work, we investigated the role of human PPC during a three-class hand gesture task using stereoelectroencephalography (SEEG) recordings from 25 subjects. Using the high gamma power (55–150 Hz) of SEEG signal recorded within three ROIs [PPC, postcentral cortex (POC) and precentral cortex (PRC)], we computed four indices for each of ROI, including: (1) activation strength; (2) gesture selectivity; (3) first activation time; (4) decoding accuracy. We find that a majority (L: 60%, R: 40%) of electrodes in all three ROIs present significant activation during the task. The activation of PPC, from a large temporal scale, is earlier than the sensorimotor cortex (PRC and POC). Among the activated electrodes, 15% (PRC), 26% (POC) and 4% (left PPC) of electrodes are significantly selective to gestures. Finally, decoding accuracy obtained by combining the selective electrodes from PPC with the sensorimotor cortex together is 5% higher than that from sensorimotor cortex only. Above all, our results suggest that PPC could be a rich neural source for iEEG-based BMI. The early activation of PPC may provide additional implications for further scientific research and high-level BMI applications.