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 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing
- Biomedical Engineering
- Health Informatics
Cite this
Mahmoudi, B., Digiovanna, J., Principe, J. C., & Sanchez, J. C. (2008). Neuronal tuning in a brain-machine interface during Reinforcement Learning. In Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology" (pp. 4491-4494). [4650210]