TY - GEN
T1 - Brain-machine interface control via reinforcement learning
AU - DiGiovanna, Jack
AU - Mahmoudi, Babak
AU - Mitzelfelt, Jeremiah
AU - Sanchez, Justin C.
AU - Principe, Jose C.
PY - 2007/9/25
Y1 - 2007/9/25
N2 - We investigate the capabilities of reinforcement learning (RL) to create a brain-machine interface (BMI) that uses Q(λ) learning to find the functional mapping between neural activity and intended behavior. This paradigm shift is intended to address the issue of paralyzed and amputee patients whom are physically unable to move, which is necessary to train traditional supervised learning BMIs. We created a RLBMI architecture incorporating a rat behavioral paradigm for prosthetic arm control. The performance results show 'proof of concept' that RLBMI can learn the temporal structure of neural signals to control a prosthetic arm.
AB - We investigate the capabilities of reinforcement learning (RL) to create a brain-machine interface (BMI) that uses Q(λ) learning to find the functional mapping between neural activity and intended behavior. This paradigm shift is intended to address the issue of paralyzed and amputee patients whom are physically unable to move, which is necessary to train traditional supervised learning BMIs. We created a RLBMI architecture incorporating a rat behavioral paradigm for prosthetic arm control. The performance results show 'proof of concept' that RLBMI can learn the temporal structure of neural signals to control a prosthetic arm.
UR - http://www.scopus.com/inward/record.url?scp=34548751154&partnerID=8YFLogxK
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U2 - 10.1109/CNE.2007.369726
DO - 10.1109/CNE.2007.369726
M3 - Conference contribution
AN - SCOPUS:34548751154
SN - 1424407923
SN - 9781424407927
T3 - Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering
SP - 530
EP - 533
BT - Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering
T2 - 3rd International IEEE EMBS Conference on Neural Engineering
Y2 - 2 May 2007 through 5 May 2007
ER -