Reconstructing Synergy-Based Hand Grasp Kinematics from Electroencephalographic Signals

Area of Science:

• Neuroscience and Biomedical Engineering
• Motor Control and Rehabilitation

Background:

• Brain-machine interfaces (BMIs) are crucial for restoring motor function in individuals with disabilities.
• The central nervous system (CNS) may use movement synergies to simplify control of complex movements.
• Previous work demonstrated neural decoding of synergy-based hand movements for exoskeleton control.

Purpose of the Study:

• To investigate the efficacy of synergy-based brain-machine interfaces for reconstructing hand kinematics.
• To develop and validate a neural decoder using electroencephalography (EEG) and hand kinematic synergies.
• To assess the potential of this approach for controlling assistive devices.

Main Methods:

• Ten healthy participants performed six types of hand grasps while EEG data was recorded.
• Hand kinematic synergies were derived from half the participants.
• A neural decoder was developed using multivariate linear regression correlating cortical activity with hand synergies.

Main Results:

• Hand kinematics were reconstructed with an average accuracy exceeding 70% using the derived synergies and neural decoder.
• The decoder successfully generalized to reconstruct movements in participants from whom synergies were not initially derived.

Conclusions:

• Synergy-based BMIs show significant potential for accurately reconstructing hand movements from EEG signals.
• This approach could be applied to develop advanced assistive devices for individuals with upper limb motor deficits, including stroke survivors.
• Further research is needed to address study limitations and explore clinical applications.