Sub-mm functional decoupling of electrocortical signals through closed-loop BMI learning.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Neural Engineering

Background:

• Volitional control of neural activity is fundamental to Brain-Machine Interface (BMI) systems.
• Investigating signal decoupling from closely spaced electrodes is crucial for high-resolution neural recording.

Purpose of the Study:

• To determine if subdural field potentials from microelectrodes (<1mm apart) can be decoupled using closed-loop BMI learning.
• To assess the stability and performance of novel microelectrode arrays for neural recording.

Main Methods:

• Fabrication of custom, flexible microelectrode arrays with 200 µm pitch and platinum black deposition.
• Chronic subdural implantation over the primary motor cortex (M1) in rats.
• In vivo monitoring of electrode impedance and closed-loop training for a center-out task using high gamma power (70-110 Hz).

Main Results:

• Successfully decoupled neural signals from microelectrodes separated by less than 1 mm.
• Demonstrated stable neural interfaces with consistent electrode impedance.
• Rats learned to perform a center-out task by volitionally modulating high gamma power for cursor control.

Conclusions:

• Closed-loop BMI learning can effectively decouple neural signals from adjacent microelectrodes.
• Custom microelectrode arrays show promise for stable, high-resolution neural recording and BMI applications.
• This approach facilitates precise volitional control of neural activity for advanced BMI systems.