Yuming He, Stan van der Ven, Hua-Peng Liaw, Chengyao Shi, Pietro Russo, Marios Gourdouparis, Mario Konijnenburg, Stefano Traferro, Martijn Timmermans, Carolina Mora Lopez, Pieter Harpe, Eugenio Cantatore, Elisabetta Chicca, Yao-Hong Liu
{"title":"An Event-based Neural Compressive Telemetry with >11× Loss-less Data Reduction for High-bandwidth Intracortical Brain Computer Interfaces.","authors":"Yuming He, Stan van der Ven, Hua-Peng Liaw, Chengyao Shi, Pietro Russo, Marios Gourdouparis, Mario Konijnenburg, Stefano Traferro, Martijn Timmermans, Carolina Mora Lopez, Pieter Harpe, Eugenio Cantatore, Elisabetta Chicca, Yao-Hong Liu","doi":"10.1109/TBCAS.2024.3378973","DOIUrl":null,"url":null,"abstract":"<p><p>Intracortical brain-computer interfaces offer superior spatial and temporal resolutions, but face challenges as the increasing number of recording channels introduces high amounts of data to be transferred. This requires power-hungry data serialization and telemetry, leading to potential tissue damage risks. To address this challenge, this paper introduces an event-based neural compressive telemetry (NCT) consisting of 8 channel-rotating Δ-ADCs, an event-driven serializer supporting a proposed ternary address event representation protocol, and an event-based LVDS driver. Leveraging a high sparsity of extracellular spikes and high spatial correlation of the high-density recordings, the proposed NCT achieves a compression ratio of >11.4×, while consumes only 1 μW per channel, which is 127× more efficient than state of the art. The NCT well preserves the spike waveform fidelity, and has a low normalized RMS error <23% even with a spike amplitude down to only 31 μV.</p>","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on biomedical circuits and systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TBCAS.2024.3378973","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Intracortical brain-computer interfaces offer superior spatial and temporal resolutions, but face challenges as the increasing number of recording channels introduces high amounts of data to be transferred. This requires power-hungry data serialization and telemetry, leading to potential tissue damage risks. To address this challenge, this paper introduces an event-based neural compressive telemetry (NCT) consisting of 8 channel-rotating Δ-ADCs, an event-driven serializer supporting a proposed ternary address event representation protocol, and an event-based LVDS driver. Leveraging a high sparsity of extracellular spikes and high spatial correlation of the high-density recordings, the proposed NCT achieves a compression ratio of >11.4×, while consumes only 1 μW per channel, which is 127× more efficient than state of the art. The NCT well preserves the spike waveform fidelity, and has a low normalized RMS error <23% even with a spike amplitude down to only 31 μV.