Abstract
In this research, we have used neural tuning to quantify the neural representation of prosthetic arm's actions in a new framework of BMI, which is based on Reinforcement Learning (RLBMI). We observed that through closed-loop brain control, the neural representation has changed to encode robot actions that maximize rewards. This is an interesting result because in our paradigm robot actions are directly controlled by a Computer Agent (CA) with reward states compatible with the user's rewards. Through co-adaptation, neural modulation is used to establish the value of robot actions to achieve reward.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology" |
| Pages | 4491-4494 |
| Number of pages | 4 |
| State | Published - Dec 1 2008 |
| Externally published | Yes |
| Event | 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - Vancouver, BC, Canada Duration: Aug 20 2008 → Aug 25 2008 |
Other
| Other | 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 |
|---|---|
| Country | Canada |
| City | Vancouver, BC |
| Period | 8/20/08 → 8/25/08 |
Fingerprint
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing
- Biomedical Engineering
- Health Informatics
Cite this
Neuronal tuning in a brain-machine interface during Reinforcement Learning. / Mahmoudi, Babak; Digiovanna, Jack; Principe, Jose C.; Sanchez, Justin C.
Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology". 2008. p. 4491-4494 4650210.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Neuronal tuning in a brain-machine interface during Reinforcement Learning
AU - Mahmoudi, Babak
AU - Digiovanna, Jack
AU - Principe, Jose C.
AU - Sanchez, Justin C.
PY - 2008/12/1
Y1 - 2008/12/1
N2 - In this research, we have used neural tuning to quantify the neural representation of prosthetic arm's actions in a new framework of BMI, which is based on Reinforcement Learning (RLBMI). We observed that through closed-loop brain control, the neural representation has changed to encode robot actions that maximize rewards. This is an interesting result because in our paradigm robot actions are directly controlled by a Computer Agent (CA) with reward states compatible with the user's rewards. Through co-adaptation, neural modulation is used to establish the value of robot actions to achieve reward.
AB - In this research, we have used neural tuning to quantify the neural representation of prosthetic arm's actions in a new framework of BMI, which is based on Reinforcement Learning (RLBMI). We observed that through closed-loop brain control, the neural representation has changed to encode robot actions that maximize rewards. This is an interesting result because in our paradigm robot actions are directly controlled by a Computer Agent (CA) with reward states compatible with the user's rewards. Through co-adaptation, neural modulation is used to establish the value of robot actions to achieve reward.
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UR - http://www.scopus.com/inward/citedby.url?scp=61849120260&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9781424418152
SP - 4491
EP - 4494
BT - Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"
ER -