Title
A Nonlinear Maximum Correntropy Information Filter For High-Dimensional Neural Decoding
Abstract
Neural signal decoding is a critical technology in brain machine interface (BMI) to interpret movement intention from multi-neural activity collected from paralyzed patients. As a commonly-used decoding algorithm, the Kalman filter is often applied to derive the movement states from high-dimensional neural firing observation. However, its performance is limited and less effective for noisy nonlinear neural systems with high-dimensional measurements. In this paper, we propose a nonlinear maximum correntropy information filter, aiming at better state estimation in the filtering process for a noisy high-dimensional measurement system. We reconstruct the measurement model between the high-dimensional measurements and low-dimensional states using the neural network, and derive the state estimation using the correntropy criterion to cope with the non-Gaussian noise and eliminate large initial uncertainty. Moreover, analyses of convergence and robustness are given. The effectiveness of the proposed algorithm is evaluated by applying it on multiple segments of neural spiking data from two rats to interpret the movement states when the subjects perform a two-lever discrimination task. Our results demonstrate better and more robust state estimation performance when compared with other filters.
Year
DOI
Venue
2021
10.3390/e23060743
ENTROPY
Keywords
DocType
Volume
state-observation model, high-dimensional measurements systems, correntropy
Journal
23
Issue
ISSN
Citations 
6
1099-4300
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Xi Liu100.68
Shuhang Chen213.39
Xiang Shen313.39
Xiang Zhang401.35
Yiwen Wang512.71