A neural signal processor for an implantable multi-channel cortical recording microsystem

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Implantable neural recording microsystems are crucial for understanding neural activity.
• Efficient processing and transmission of neural data are key challenges.
• Existing systems may lack the flexibility for diverse research needs.

Purpose of the Study:

• To develop a versatile 64-channel neural processor for an implantable microsystem.
• To enable detection and efficient data handling of neural action potentials.
• To provide high-resolution monitoring capabilities for specific neural signals.

Main Methods:

• Designed a 64-channel neural processor with programmable spike detection thresholds.
• Implemented scan mode for action potential detection, channel tagging, compression, and serial data streaming.
• Developed monitor mode for high-resolution, two-channel signal viewing.

Main Results:

• The processor successfully detects positive, negative, and biphasic neural spikes.
• Action potential data is accurately tagged, compressed, and formatted for external transmission.
• Monitor mode allows detailed analysis of selected neural channels.

Conclusions:

• The developed neural processor enhances capabilities for implantable neural recording.
• Its features support efficient data acquisition and flexible signal analysis.
• This technology advances neural interface research and applications.