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.
Original language | English |
---|---|
Title of host publication | Proceedings of the 2022 IEEE Biomedical Circuits and Systems Conference (BioCAS) |
Publisher | IEEE |
Pages | 80-84 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-6654-6917-3 |
ISBN (Print) | 978-1-6654-6918-0 |
DOIs | |
Publication status | Published - 2022 |
Event | 2022 IEEE Biomedical Circuits and Systems Conference (BioCAS) - Taipei, Taiwan Duration: 13 Oct 2022 → 15 Oct 2022 |
Conference
Conference | 2022 IEEE Biomedical Circuits and Systems Conference (BioCAS) |
---|---|
Country/Territory | Taiwan |
City | Taipei |
Period | 13/10/22 → 15/10/22 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- A/D conversion
- brain-machine interfaces
- compression algorithm
- neural interfaces