A Streaming PCA based VLSI Chip for Neural Data Compression

Local field potentials (LFPs) are popularly used in wireless neural interface due to its chronic stability and robustness against noise and radio interferences. On-chip data compression is advantageous that allows for integration with the recent low-power, low-data-rate wireless technologies to ensure reliable operations. In this paper, we propose a streaming principal component analysis (PCA) based algorithm and its microchip implementation to compress multichannel LFP data. The chip has been designed in a 65nm CMOS technology and occupies a silicon area of 0.06mm(2). It has been tested with guinea pig auditory cortex data recorded with a multi-shank NeuroNexus probe, where the chip can achieve an 8x compression ratio with similar to 3% average reconstruction error, consuming 144nW per channel at a 0.5V power supply.