Neural Spike Compression through Salient Sample Extraction and Curve Fitting Dedicated to High-Density Brain Implants

This work proposes a data reduction framework, specific to the compression of extra-cellular neuronal action potentials on brain-implantable microsystems. The proposed framework significantly reduces the extent of data representing spike waveforms, paving the way for the implementation of next-generation, high-density neural recording brain implants. This highly-compressive approach picks a small number of salient samples of the spike, using which and based on some predefined functions the entire spike waveshape is formulated. The amplitudes and timings of the salient samples are sent off the implant in order to reconstruct the spike waveshape on the external side of the system. In addition to exhibiting extremely high data compression capability, this technique is highly hardware efficient, hence it well suits for brain-implantable neural recording microsystems with high channel counts. Based on the proposed framework, a 128-channel neural signal compressor is designed and microfabricated using the TSMC 130-nm CMOS technology, and measures 1.05 mm by 0.35 mm, giving an area-per-channel of 0.00287 mm2. The circuit is tested using a library of intra-cortically recorded neural signals. At an average spike firing rate of 8 Spike/s, the circuit temporally reduces neural data with an average compression rate of ~272, which is equivalent to a true compression rate of ~2176. Operated using a 1-V power supply and at a clock rate of 32 MHz, the 128-channel neural data compressor consumes 0.164 µW/channel.