Compressive sensing of neural action potentials using nano platinum black modified microelectrode array

Long-term wireless neural recording systems which are subject to stringent power consumption, are highly desired to reduce the rate of data transmission and computation complexity. In this paper, we propose using a combination of onchip neural action potentials (`spikes´) detection system and compressive sensing (CS) techniques to reduce the power required for data transmission and a random Bernoulli matrix to reduce the computation complexity consequently further reduce the power consumption. By analyzing the data detected by nano platinum black modified microelectrode array implanted in the hippocampus of the Sprague-Dawley (SD) rat, we prove that spikes are compressible in the wavelet domain. We use the Bayesian CS algorithm to reconstruct them. Our results show that the mean compression ratio is 26:1 achieved for 16-dB SNDR recovery using this mechanism.