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A Brain-Spinal Interface (BSI) System-on-Chip (SoC) for Closed-Loop Cortically-Controlled Intraspinal
Shahab Shahdoost1, Shawn B Frost2, David J Guggenmos2
1Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH 44106 USA.
Analog Integrated Circuits and Signal Processing
|June 4, 2021
Summary
This study presents a miniaturized brain-spinal interface (BSI) for closed-loop control of intraspinal microstimulation (ISMS). The system translates real-time neural signals into muscle activation patterns in rats with spinal cord injuries.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Implantable Devices
Background:
- Spinal cord injuries often lead to motor deficits.
- Restoring motor function requires precise neural control.
- Current brain-spinal interfaces face challenges in miniaturization and real-time processing.
Purpose of the Study:
- To develop a fully miniaturized brain-spinal interface (BSI) system.
- To enable closed-loop, cortically-controlled intraspinal microstimulation (ISMS).
- To demonstrate the system's efficacy in a rodent spinal cord injury model.
Main Methods:
- A system-on-chip (SoC) was fabricated using advanced CMOS technology.
- The SoC integrates spike-recording, digital signal processing (DSP), and stimulation modules.
- Real-time neural spike discrimination and ISMS triggering were implemented.
- The system was validated in a rat model with T8 spinal cord contusion injury.
Main Results:
- The miniaturized BSI system successfully recorded intracortical neural spikes.
- Real-time processing enabled the generation of ISMS trigger patterns.
- Stimulation of the lumbar spinal cord elicited distinct hindlimb muscle activation patterns.
- The closed-loop system demonstrated effective cortically-controlled stimulation.
Conclusions:
- A fully miniaturized BSI system for closed-loop ISMS was successfully developed and validated.
- The system shows potential for restoring motor function after spinal cord injury.
- This technology advances the development of implantable neural interfaces for therapeutic applications.
