Data Compression versus Signal Fidelity Trade-off in Wired-OR ADC Arrays for Neural Recording
Pumiao Yan (Stanford University)
Nishal P. Shah (Stanford University)
Dante Muratore (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Pulkit Tandon (Stanford University)
E.J. Chichilnisky (Stanford University)
Boris Murmann (Stanford University)
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Abstract
This paper investigates the efficacy of a wired-OR compressive readout architecture for neural recording, which enables simultaneous data compression of action potential signals for high channel count electrode arrays. We consider a range of wiring configurations to assess the trade-offs between compression ratio and various task-specific signal fidelity metrics. We consider the fidelity in threshold crossing detection, spike assignment, and waveform estimation, and find that for an event SNR of 7-10 the readout captures at least 80% of the spike waveforms at ∼150x data compression.