{"title":"A Fully Implantable, Programmable and Multimodal Neuroprocessor for Wireless, Cortically Controlled Brain-Machine Interface Applications.","authors":"Fei Zhang, Mehdi Aghagolzadeh, Karim Oweiss","doi":"10.1007/s11265-012-0670-x","DOIUrl":null,"url":null,"abstract":"<p><p>Reliability, scalability and clinical viability are of utmost importance in the design of wireless Brain Machine Interface systems (BMIs). This paper reports on the design and implementation of a neuroprocessor for conditioning raw extracellular neural signals recorded through microelectrode arrays chronically implanted in the brain of awake behaving rats. The neuroprocessor design exploits a sparse representation of the neural signals to combat the limited wireless telemetry bandwidth. We demonstrate a multimodal processing capability (monitoring, compression, and spike sorting) inherent in the neuroprocessor to support a wide range of scenarios in real experimental conditions. A wireless transmission link with rate-dependent compression strategy is shown to preserve information fidelity in the neural data. At 32 channels, the neuroprocessor has been fully implemented on a 5mm×5mm nano-FPGA, and the prototyping resulted in 5.19 mW power consumption, bringing its performance within the power-size constraints for clinical use. The optimal design for compression and sorting performance was evaluated for multiple sampling frequencies, wavelet basis choice and power consumption.</p>","PeriodicalId":50050,"journal":{"name":"Journal of Signal Processing Systems for Signal Image and Video Technology","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11265-012-0670-x","citationCount":"22","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Signal Processing Systems for Signal Image and Video Technology","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11265-012-0670-x","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2011/6/15 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 22
Abstract
Reliability, scalability and clinical viability are of utmost importance in the design of wireless Brain Machine Interface systems (BMIs). This paper reports on the design and implementation of a neuroprocessor for conditioning raw extracellular neural signals recorded through microelectrode arrays chronically implanted in the brain of awake behaving rats. The neuroprocessor design exploits a sparse representation of the neural signals to combat the limited wireless telemetry bandwidth. We demonstrate a multimodal processing capability (monitoring, compression, and spike sorting) inherent in the neuroprocessor to support a wide range of scenarios in real experimental conditions. A wireless transmission link with rate-dependent compression strategy is shown to preserve information fidelity in the neural data. At 32 channels, the neuroprocessor has been fully implemented on a 5mm×5mm nano-FPGA, and the prototyping resulted in 5.19 mW power consumption, bringing its performance within the power-size constraints for clinical use. The optimal design for compression and sorting performance was evaluated for multiple sampling frequencies, wavelet basis choice and power consumption.
期刊介绍:
The Journal of Signal Processing Systems for Signal, Image, and Video Technology publishes research papers on the design and implementation of signal processing systems, with or without VLSI circuits. The journal is published in twelve issues and is distributed to engineers, researchers, and educators in the general field of signal processing systems.