An Iterative Spike Compression Method Based on Wavelet Transform and Heuristic Algorithm

Due to the constraints of bandwidth and power in implantable brain-computer interface, large amount of neural data cannot be easily transferred over a wireless link. Wavelet-based compression before transmission is a popular technique for reducing the data rate. But it struggles to achieve a well-balanced trade-off between compression ratio and reconstruction error. In this article, we propose an iterative spike compression method based on discrete wavelet transform and heuristic algorithm. It can automatically search for the optimal combination of wavelet coefficients, trading longer processing time for higher compression ratio and lower reconstruction error. It has been validated in simulation that the proposed algorithm achieves a compression ratio of 9.25 on detected spike data with a reconstruction error of 3.63%. The proposed processor has been validated in the Cadence Virtuoso Environment using X-FAB’s 180nm CMOS process. It occupies a chip area of $2.56 \mathrm{~mm}^{2}$ and consumes $385 \mu \mathrm{~W}$ power.