TY - GEN
T1 - A new architecture for deriving dynamic brain-machine interfaces
AU - Fortes, José
AU - Figueiredo, Renato
AU - Hermer-Vazquez, Linda
AU - Príncipe, José
AU - Sanchez, Justin C.
PY - 2006/1/1
Y1 - 2006/1/1
N2 - Great potential exists for future Brain Machine Interfaces (BMIs) to help paralyzed patients, and others with motor disabilities, regain (artificial) motor control and autonomy. This paper describes a novel approach towards the development of new design architectures and research test-beds for advanced BMls. It addresses a critical design challenge in deriving the functional mapping between the subject's movement intent and actuated behavior. Currently, adaptive signal processing techniques are used to correlate neuronal modulation with known movements generated by the subject. However, with patients who are paralyzed, access to the individual's movement is unavailable. Inspired by motor control research, this paper considers a predictive framework for BMI using multiple adaptive models trained with supervised or reinforcement learning in a closed-loop architecture that requires real-time feedback. Here, movement trajectories can be inferred and incrementally updated using instantaneous knowledge of the movement target and the individual's current neuronal activation. In this framework, BMIs require a computing infrastructure capable of selectively executing multiple models on the basis of signals received by and/or provided to the brain in real time. Middleware currently under investigation to provide this data-driven dynamic capability is discussed.
AB - Great potential exists for future Brain Machine Interfaces (BMIs) to help paralyzed patients, and others with motor disabilities, regain (artificial) motor control and autonomy. This paper describes a novel approach towards the development of new design architectures and research test-beds for advanced BMls. It addresses a critical design challenge in deriving the functional mapping between the subject's movement intent and actuated behavior. Currently, adaptive signal processing techniques are used to correlate neuronal modulation with known movements generated by the subject. However, with patients who are paralyzed, access to the individual's movement is unavailable. Inspired by motor control research, this paper considers a predictive framework for BMI using multiple adaptive models trained with supervised or reinforcement learning in a closed-loop architecture that requires real-time feedback. Here, movement trajectories can be inferred and incrementally updated using instantaneous knowledge of the movement target and the individual's current neuronal activation. In this framework, BMIs require a computing infrastructure capable of selectively executing multiple models on the basis of signals received by and/or provided to the brain in real time. Middleware currently under investigation to provide this data-driven dynamic capability is discussed.
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U2 - 10.1007/11758532_72
DO - 10.1007/11758532_72
M3 - Conference contribution
AN - SCOPUS:33746601336
SN - 3540343830
SN - 9783540343837
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 546
EP - 553
BT - Computational Science - ICCS 2006
PB - Springer Verlag
T2 - ICCS 2006: 6th International Conference on Computational Science
Y2 - 28 May 2006 through 31 May 2006
ER -