Nazanin Ahmadi Dastgerdi, Hossein Hoseini-Nejad, H. Amiri
{"title":"Neural Spike Compression Based on Split Vector Quantization for Implantable BMIs","authors":"Nazanin Ahmadi Dastgerdi, Hossein Hoseini-Nejad, H. Amiri","doi":"10.1109/IranianCEE.2019.8786761","DOIUrl":null,"url":null,"abstract":"This paper reports a novel spike compression approach for implantable intra-cortical neural recording microsystems based on Split Vector Quantization (SVQ). The proposed method presents a spike compression ratio 14.8 at the cost of classification accuracy (CA). The average value of CA is 94% over a wide range (7 to 15) of signal to noise ratios (SNR) of the neural signal.","PeriodicalId":6683,"journal":{"name":"2019 27th Iranian Conference on Electrical Engineering (ICEE)","volume":"24 1","pages":"1779-1782"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 27th Iranian Conference on Electrical Engineering (ICEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IranianCEE.2019.8786761","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract
This paper reports a novel spike compression approach for implantable intra-cortical neural recording microsystems based on Split Vector Quantization (SVQ). The proposed method presents a spike compression ratio 14.8 at the cost of classification accuracy (CA). The average value of CA is 94% over a wide range (7 to 15) of signal to noise ratios (SNR) of the neural signal.
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