Hybrid EEG-EMG Control Scheme for Multiple Degrees of Freedom Upper-Limb Prostheses

Upper-limb motor disabilities and amputation pose a significant burden on individuals, hindering their ability to perform daily activities independently. While various research studies aim to enhance the performance of current upper-limb prosthetic devices, electrically activated prostheses still face challenges in achieving optimal functionality. This paper explores the potential of utilizing electromyogram (EMG) and electroencephalogram (EEG) signals to not only decipher movement across multiple degrees of freedom (DOFs) but also offer a more intuitive means of control. In this study, six distinct control schemes for upper-limb prosthetic devices are proposed, each with different combinations of EEG and EMG signals. These schemes were designed to control multiple degrees-of-freedom movements, encompassing five different hand and forearm actions (hand-open, hand-close, wrist pronation, wrist supination, and rest-state). Using Linear Discriminant Analysis as a model results in classification accuracies of over 85% for combined EEG-EMG control schemes. The results suggest promising advancements in the field and show the potential for a more effective and user-friendly control interface for upper-limb prosthetic devices.