Towards neuromorphic compression based neural sensing for next-generation wireless implantable brain machine interface

This work introduces a neuromorphic compression based neural sensing architecture with address-event representation inspired readout protocol for massively parallel, next-gen wireless implantable brain machine interface (iBMI). The architectural trade-offs and implications of the proposed method are quantitatively analyzed in terms of compression ratio (CR) and spike information preservation. For the latter, we used metrics such as root-mean-square error and correlation coefficient (CC) between the original and recovered signals to assess the effect of neuromorphic compression on the spike shape. Furthermore, we use accuracy, sensitivity, and false detection rate to understand the effect of compression on downstream iBMI tasks, specifically, spike detection. We demonstrate that a data CR of 15-265 per channel can be achieved by transmitting address-event pulses for two different biological datasets. The CR further increases to 200- 50K per channel, 50 x more than in prior works, by the selective transmission of event pulses corresponding to neural spikes. A CC of approximate to 0.9 and spike detection accuracy of over 90% were obtained for the worst-case analysis involving 10K-channel simulated recording and typical analysis using 100 or 384-channel real neural recordings. We also analyzed the collision handling capability for up to 10K channels and observed no significant error, indicating the scalability of the proposed pipeline. We also present initial results to show the ability of intention decoders to work directly on the events generated by the neuromorphic front-end.