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A closed-loop compressive-sensing-based neural recording system.
Jie Zhang1, Srinjoy Mitra, Yuanming Suo
1Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA.
Journal of Neural Engineering
|April 16, 2015
Summary
This study presents a low-power, closed-loop compressive sensing (CS) neural recording system. It significantly reduces data bandwidth and power consumption for implantable devices, achieving over 10x data compression.
Area of Science:
- Neuroscience
- Electrical Engineering
- Biomedical Engineering
Background:
- Implantable neural recording devices require efficient data transmission to minimize power consumption.
- Reducing data bandwidth is crucial for large-scale neural recording systems.
Purpose of the Study:
- To describe a low-power, closed-loop compressive sensing (CS) based neural recording system.
- To demonstrate a scalable solution for reducing power consumption in neural recording devices.
Main Methods:
- An application-specific integrated circuit (ASIC) with 4 recording channels utilizing CS circuits was developed.
- A real-time, off-chip CS recovery block with adaptive compression rate adjustment was implemented.
- In vivo and public neural databases were used for system testing.
Main Results:
- Achieved >10x data compression on the neural spike band (500-6KHz) with minimal additional power consumption (0.83uW/electrode).
- Further compression of detected spikes by ~16x was possible, maintaining >95% spike classification success rate.
- The CS circuit occupied 0.11mm²/electrode in a 180nm CMOS process, with total power consumption <16uW/electrode.
Conclusions:
- The system's power and area efficiency make it suitable for large-scale neural recording arrays.
- Closed-loop operation and robust reconstruction enhance the practicality of CS for long-term neural recording.
