Decoding Synergy-Based Hand Movements using Electroencephalography.

In this paper, scalp electroencephalographic (EEG) signals were recorded from 10 subjects during hand grasping. Six objects that span different grasp types were used. Grasp kinematics were recorded using CyberGlove. From a training subset of the data, kinematic synergies were determined and their reconstruction weights in these grasps were calculated. EEG features (power spectral densities in four low and high frequency bands) were trained on kinematic synergy weights using multivariate linear regression. Using this model, kinematics from testing subset of data were decoded from EEG with 3-fold cross validation. Results are compared to chance level to determine if reconstruction weights are related to EEG features. Results indicate that EEG features can decode synergy-based movement generation. Study implications and future implementations were discussed.