TY - GEN
T1 - BMI cyberworkstation
T2 - 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
AU - Zhao, Ming
AU - Rattanatamrong, Prapaporn
AU - DiGiovanna, Jack
AU - Mahmoudi, Babak
AU - Figueiredo, Renato J.
AU - Sanchez, Justin C.
AU - Príncipe, José C.
AU - Fortes, José A.B.
PY - 2008
Y1 - 2008
N2 - Dynamic data-driven brain-machine interfaces (DDDBMI) have great potential to advance the understanding of neural systems and improve the design of brain-inspired rehabilitative systems. This paper presents a novel cyberinfrastructure that couples in vivo neurophysiology experimentation with massive computational resources to provide seamless and efficient support of DDDBMI research. Closed-loop experiments can be conducted with in vivo data acquisition, reliable network transfer, parallel model computation, and real-time robot control. Behavioral experiments with live animals are supported with real-time guarantees. Offline studies can be performed with various configurations for extensive analysis and training. A Web-based portal is also provided to allow users to conveniently interact with the cyberinfrastructure, conducting both experimentation and analysis. New motor control models are developed based on this approach, which include recursive least square based (RLS) and reinforcement learning based (RLBMI) algorithms. The results from an online RLBMI experiment shows that the cyberinfrastructure can successfully support DDDBMI experiments and meet the desired real-time requirements.
AB - Dynamic data-driven brain-machine interfaces (DDDBMI) have great potential to advance the understanding of neural systems and improve the design of brain-inspired rehabilitative systems. This paper presents a novel cyberinfrastructure that couples in vivo neurophysiology experimentation with massive computational resources to provide seamless and efficient support of DDDBMI research. Closed-loop experiments can be conducted with in vivo data acquisition, reliable network transfer, parallel model computation, and real-time robot control. Behavioral experiments with live animals are supported with real-time guarantees. Offline studies can be performed with various configurations for extensive analysis and training. A Web-based portal is also provided to allow users to conveniently interact with the cyberinfrastructure, conducting both experimentation and analysis. New motor control models are developed based on this approach, which include recursive least square based (RLS) and reinforcement learning based (RLBMI) algorithms. The results from an online RLBMI experiment shows that the cyberinfrastructure can successfully support DDDBMI experiments and meet the desired real-time requirements.
UR - http://www.scopus.com/inward/record.url?scp=61849175213&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=61849175213&partnerID=8YFLogxK
U2 - 10.1109/iembs.2008.4649235
DO - 10.1109/iembs.2008.4649235
M3 - Conference contribution
C2 - 19162738
SN - 9781424418152
T3 - Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"
SP - 646
EP - 649
BT - Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
PB - IEEE Computer Society
Y2 - 20 August 2008 through 25 August 2008
ER -