An implantable, rechargeable neuromodulation research tool using a distributed interface and algorithm architecture

作者: Duane Bourget , Hank Bink , Scott Stanslaski , David Linde , Chris Arnett

DOI: 10.1109/NER.2015.7146560

关键词: Interface (computing)Embedded systemVerification and validationSystems architectureComputer scienceUse caseScientific instrumentFlexibility (engineering)Artificial neural networkComputer data storage

摘要: Implantable medical devices can provide chronic access to the nervous system. Implants containing embedded scientific instrumentation payloads (e.g. - sensors, classification, and control policy implementation) a unique opportunity for exploring diseased neural networks how these may be better treated. Physically embedding in an implant creates intertwined constraints such as power consumption, algorithmic computation limits lack of flexibility, data storage, scale sensing information. These limitations largely addressed with combination rechargeable batteries high-bandwidth, secure, distance telemetry, which enables distributed research Taking advantage architecture helps facilitate investigation more unconstrained environment. In this paper, we describe design implantable tool, discuss prototype system its details, present preliminary bench verification validation human drawn from representative use cases.

参考文章(8)
Takamitsu Yamamoto, Yoichi Katayama, Junichi Ushiba, Hiroko Yoshino, Toshiki Obuchi, Kazutaka Kobayashi, Hideki Oshima, Chikashi Fukaya, On-demand control system for deep brain stimulation for treatment of intention tremor. Neuromodulation. ,vol. 16, pp. 230- 235 ,(2013) , 10.1111/J.1525-1403.2012.00521.X
C. de Hemptinne, E. S. Ryapolova-Webb, E. L. Air, P. A. Garcia, K. J. Miller, J. G. Ojemann, J. L. Ostrem, N. B. Galifianakis, P. A. Starr, Exaggerated phase–amplitude coupling in the primary motor cortex in Parkinson disease Proceedings of the National Academy of Sciences of the United States of America. ,vol. 110, pp. 4780- 4785 ,(2013) , 10.1073/PNAS.1214546110
Ishita Basu, Daniel Graupe, Daniela Tuninetti, Pitamber Shukla, Konstantin V Slavin, Leo Verhagen Metman, Daniel M Corcos, Pathological tremor prediction using surface electromyogram and acceleration: potential use in 'ON-OFF' demand driven deep brain stimulator design. Journal of Neural Engineering. ,vol. 10, pp. 036019- 036019 ,(2013) , 10.1088/1741-2560/10/3/036019
Peng Cong, Piyush Karande, Jonathan Landes, Rob Corey, Scott Stanslaski, Wes Santa, Randy Jensen, Forrest Pape, Dan Moran, Tim Denison, A 32-channel modular bi-directional neural interface system with embedded DSP for closed-loop operation european solid state circuits conference. pp. 99- 102 ,(2014) , 10.1109/ESSCIRC.2014.6942031
Mark J Cook, Terence J O'Brien, Samuel F Berkovic, Michael Murphy, Andrew Morokoff, Gavin Fabinyi, Wendyl D'Souza, Raju Yerra, John Archer, Lucas Litewka, Sean Hosking, Paul Lightfoot, Vanessa Ruedebusch, W Douglas Sheffield, David Snyder, Kent Leyde, David Himes, Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. Lancet Neurology. ,vol. 12, pp. 563- 571 ,(2013) , 10.1016/S1474-4422(13)70075-9
Simon Little, Alex Pogosyan, Spencer Neal, Baltazar Zavala, Ludvic Zrinzo, Marwan Hariz, Thomas Foltynie, Patricia Limousin, Keyoumars Ashkan, James FitzGerald, Alexander L. Green, Tipu Z. Aziz, Peter Brown, Adaptive deep brain stimulation in advanced Parkinson disease Annals of Neurology. ,vol. 74, pp. 449- 457 ,(2013) , 10.1002/ANA.23951
A G Rouse, S R Stanslaski, P Cong, R M Jensen, P Afshar, D Ullestad, R Gupta, G F Molnar, D W Moran, T J Denison, A chronic generalized bi-directional brain–machine interface Journal of Neural Engineering. ,vol. 8, pp. 036018- 036018 ,(2011) , 10.1088/1741-2560/8/3/036018