Electrocorticographic Dynamics Predict Sustained Grasping and Upper-Limb Kinetic Output

• Published in: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) pp. 110 - 114
• Main Authors: Ly, Katie K., Wu, Jing, Levinson, Lila H., Shuman, Benjamin R., Steele, Katherine M., Ojemann, Jeffrey G., Rao, Rajesh P.N.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2018
• Subjects: Electrodes; Force; Grasping; Kinematics; Kinetic Motor Control; Kinetic theory; Posterior Parietal Cortex; Precuneus; Task analysis; Temporoparietal Junction; Time series analysis
• ISSN: 2577-1655
• DOI: 10.1109/SMC.2018.00029

Determining hand grasping forces and kinetic direction and magnitude are important for the development of dexterous neural prosthetics. While many earlier decoding methods have successfully predicted upper-limb kinematic output from cortical signals in the sensorimotor parietal and premotor regions, the full extent of the regions that characterize kinetic behavior is unknown. In this study, we found that neural dynamics based on electrocorticography (ECoG) recorded from the human brain surface can successfully encode structured and unstructured grasping and arm kinetic output. We found a time-averaged linear relationship between gamma band spectral ECoG power and sustained grasping force output with visual feedback. In the kinetic grasping task, we obtained classification accuracy of 47% (25% = chance) using quadratic discriminant analysis. Additionally, we also found a similar linear relationship between spectral power and cued isometric force generation, without concurrent visual feedback, in arm force application; this feature could also classify arm force output categories with an accuracy of 41% (33% = chance). In addition, we applied quadratic discriminant analysis with top 12 principal components to attain approximately 26% accuracy (chance is 16%) in determining arm kinetic force direction. We found that the gamma band spectral power from both experiments in posterior parietal cortex, as well as projections of high gamma variability along the top principal components, can be successfully used to predict sustained kinetic outputs and to explain both structured and unstructured force output variability. Our findings contribute to a deeper understanding of neural dynamics for fine kinetic behavior.