Decoding and generating synergy-based hand movements using electroencephalography during motor execution and motor imagery

Abstract

Brain-machine interfaces (BMIs) have proven valuable in motor control and rehabilitation. Motor imagery (MI) is a key tool for developing BMIs, particularly for individuals with impaired limb function. Motor planning and internal programming are hypothesized to be similar during motor execution (ME) and motor imagination. The anatomical and functional similarity between motor execution and motor imagery suggests that synergy-based movement generation can be achieved by extracting neural correlates of synergies or movement primitives from motor imagery. This study explored the feasibility of synergy-based hand movement generation using electroencephalogram (EEG) from imagined hand movements. Ten subjects participated in an experiment to imagine and execute hand movement tasks while their hand kinematics and neural activity were recorded. Hand kinematic synergies derived from executed movements were correlated with EEG spectral features to create a neural decoding model. This model was used to decode the weights of kinematic synergies from motor imagery EEG. These decoded weights were then combined with kinematic synergies to generate hand movements. As a result, the decoding model successfully predicted hand joint angular velocity patterns associated with grasping different objects. This adaptability demonstrates the model's ability to capture the motor control characteristics of ME and MI, advancing our understanding of MI-based neural decoding. The results hold promise for potential applications in noninvasive synergy-based neuromotor control and rehabilitation for populations with upper limb motor disabilities.