Gesture encoding in human left precentral gyrus neuronal ensembles.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Rehabilitation Technology

Background:

• Dexterous upper limb motion is controlled by complex cortical activity patterns.
• Limited mobility affects a broad clinical population, necessitating advanced assistive technologies.
• Brain-computer interface (BCI) technology offers a potential solution for restoring motor function.

Purpose of the Study:

• To investigate cortical activity patterns associated with dexterous hand gestures.
• To assess the feasibility of using neural signals for controlling upper limb prosthetics or orthotics.
• To determine the potential of intracortical BCI for generating diverse command signals.

Main Methods:

• Recorded neural activity from motor cortical ensembles using microelectrode arrays in two participants with cervical spinal cord injury.
• Participants attempted to perform 48 distinct hand gestures.
• Analyzed neural ensemble activity to decode gesture intentions.

Main Results:

• Achieved approximately 70% classification accuracy for all 48 gestures and ~90% for sets of 10 gestures.
• Demonstrated that neural latent spaces were unique to each participant.
• Showcased the potential for single-hemisphere recordings to generate signals for both hands.

Conclusions:

• Single unit ensemble activity in the human precentral gyrus can generate a wide range of gesture-related signals.
• These findings support the development of intuitive and diverse command signals for intracortical BCI applications.
• This research holds promise for improving the quality of life for individuals with limited mobility.