Optimal decentralized Kalman filter and Lainiotis filter

Assimakis, Nicholas; Αδάμ, Μαρία; Koziri, Maria; Voliotis, Stamatis; Asimakis, Konstantinos

doi:10.1016/j.dsp.2012.08.005

Cited by 11 publications

(4 citation statements)

References 31 publications

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“…where 𝑥 𝑡−1|𝑡−1 denotes the posterior prosthetic state output from the previous time step 𝑡 − 1. The information matrix of the prior state is denoted as 𝐼 𝑡|𝑡−1 ∈ ℝ 𝐷×𝐷 , which is derived from the posterior information matrix at the previous time 𝐼 𝑡−1|𝑡−1 [38].…”

Section: ) Adaptive Decoding During the Brain Control Processmentioning

confidence: 99%

Kernel Reinforcement Learning-Assisted Adaptive Decoder Facilitates Stable and Continuous Brain Control Tasks

Zhang,

Chen,

Wang

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Brain-Machine Interfaces (BMIs) assist paralyzed people to brain control (BC) the neuro-prosthesis continuously moving in space. During the BC process, the subject imagines the movement of the real limb and adapts the brain activity according to the sensory feedback. The neural adaptation in the closed-loop control results in complex and changing brain signals. Simultaneously, the decoder interprets the time-varying functional mapping between neural activity and continuous trajectory. It is crucial and challenging to accurately and adaptively track the mapping to help the subject accomplish the BC task with a stable performance. Existing Kalman Filter (KF) based decoders achieve continuous trajectory control by linearly interpreting neural firing observations into self-evolving prosthetic states. However, the linear neural-state mapping might not accurately reflect the movement intention of the subject. In this paper, we propose a novel method that allows subjects to achieve continuous brain control efficiently and stably. The proposed method incorporates a kernel reinforcement learning method into a state-observation model to decode the nonlinearly neural observation into a continuous trajectory state. The state transition function ensures the continuity of the prosthetic state. The kernel reinforcement learning allows the quick adaptation of the nonlinear neural-movement mapping during the BC process. The proposed method is tested in an online brain control reaching task for rats. Compared with KF, our method achieved more successful trials, faster response time, shorter inter-trial time, and remained stable over days. These results demonstrate that the proposed method is an efficient tool to assist subjects in brain control tasks.

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Section: ) Adaptive Decoding During the Brain Control Processmentioning

confidence: 99%

Kernel Reinforcement Learning-Assisted Adaptive Decoder Facilitates Stable and Continuous Brain Control Tasks

Zhang,

Chen,

Wang

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…where α i, j is a non-negative, real coefficient satisfying N j=1 α i, j = 1 that assigns different weights to different neighbors. In order to minimize (2) in an online manner, we employ the stochastic gradient approach [24]. To this end, we calculate the gradient for (2) as…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Considering that we are optimizing a sum of two convex cost functions in (2) with the use of (4), we note that we can carry out the optimization using incremental solutions over (2) where the update is performed in two steps. Since we consider the adapt-thencombine (ATC) diffusion strategy for this paper, first we create an intermediate estimate by using the gradient of the first summand in (2) and then update the estimate using the second summand in (2) as [17]…”

Section: Problem Descriptionmentioning

confidence: 99%

“…In tandem with the increasing computational capabilities of processing units and the growing amount of generated data, the demand on distributed networks and decentralized data processing algorithms have remained an area of growing interest [1][2][3]. With intrinsic characteristics such as robustness and scalability, distributed architectures provide enhanced efficiency and performance for a wide variety of applications ranging from adaptive filtering, sequential detection, sensor networks, to distributed resource allocation [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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Resource-aware event triggered distributed estimation over adaptive networks

Utlu

Kılıç

Kozat

2017

Digital Signal Processing

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We propose a novel algorithm for distributed processing applications constrained by the available communication resources using diffusion strategies that achieves up to a 10 3 fold reduction in the communication load over the network, while delivering a comparable performance with respect to the state of the art. After computation of local estimates, the information is diffused among the processing elements (or nodes) non-uniformly in time by conditioning the information transfer on level-crossings of the diffused parameter, resulting in a greatly reduced communication requirement. We provide the mean and mean-square stability analyses of our algorithms, and illustrate the gain in communication efficiency compared to other reduced-communication distributed estimation schemes.

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