A Time-Domain Analog Spatial Compressed Sensing Encoder for Multi-Channel Neural Recording

Area of Science:

• Neuroscience
• Electrical Engineering
• Signal Processing

Background:

• The increasing channel count in silicon neural probes necessitates efficient data compression.
• Neural signals, like action potentials (APs), possess wide bandwidths requiring advanced data handling.
• MEMS technology advancements drive the need for higher-density neural recording systems.

Purpose of the Study:

• To propose a novel time-domain analog spatial compressed sensing (CS) encoder for neural recording.
• To develop a data reduction technique suitable for high-density neural recording arrays.
• To design a simpler and lower-power CS encoder compared to existing methods.

Main Methods:

• Employed compressed sensing (CS) for neural data reduction.
• Developed a novel time-domain analog CS encoder architecture.
• Fabricated a prototype encoder using a 180 nm 1P6M CMOS process.

Main Results:

• Achieved an active area of 0.0342 mm²/channel.
• Demonstrated an energy efficiency of 25.0 pJ/channel·conversion.
• The proposed encoder offers a simpler and lower-power circuit design.

Conclusions:

• The novel time-domain analog CS encoder is effective for neural data compression.
• The fabricated prototype shows significant improvements in area and energy efficiency.
• This technology addresses the data handling challenges in high-density neural recording applications.