Identification of Arm Movements Using Electrocorticographic Signals

The purpose of this study was to explore the possibility of using electrocorticographic (ECoG) recordings to identify the upper limb motion performed by a human subject. More specifically, we were trying to identify features in the ECoG signals that could help us determine the type of movement performed by an individual. Two subjects with subdural electrodes implanted over the primary motor cortex were asked to perform various motor tasks with the upper limb contralateral to the site of electrode implantation. ECoG signals and upper limb kinematics were recorded simultaneously while the participants were performing the movements. ECoG frequency components were identified that correlated well with the performed movements measured along 3D coordinates (X, Y, and Z). These frequencies were grouped using histograms. The resulting histograms had consistent and unique shapes that were representative of specific upper limb movements performed by the participants. Thus, it was possible to identify which movement was performed. To confirm these findings a nearest neighbour classifier was applied to identify the specific movement that each individual had performed. The achieved classification accuracy was 89%.