Predicting hand forces from scalp electroencephalography during isometric force production and object grasping

Area of Science:

• Neuroscience
• Biomedical Engineering
• Rehabilitation Technology

Background:

• Understanding the neural control of hand forces is crucial for developing advanced neuroprosthetics.
• Scalp electroencephalography (EEG) offers a non-invasive method to record brain activity.

Purpose of the Study:

• To investigate the feasibility of predicting hand forces from scalp EEG signals.
• To explore the relationship between brain activity and different hand force tasks.

Main Methods:

• Ten able-bodied subjects performed isometric force production and grasp-and-lift tasks.
• Electroencephalography (EEG) data was recorded from scalp electrodes.
• Decoding accuracies were calculated to assess prediction performance.

Main Results:

• EEG electrodes over central scalp areas showed high correlation with force rate trajectories.
• EEG signal patterns in central sites resembled force rate, not force, trajectories.
• Grasp-and-lift tasks yielded higher decoding accuracies (median r=0.51) than isometric tasks (median r=0.35).

Conclusions:

• Scalp EEG can predict hand forces, particularly force rate.
• The findings support the potential use of EEG in closed-loop neuroprosthetic control for hand function.
• This research advances understanding of the neural representation of hand force control.