{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,14]],"date-time":"2024-07-14T04:10:29Z","timestamp":1720930229046},"reference-count":72,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,6,12]],"date-time":"2021-06-12T00:00:00Z","timestamp":1623456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shenzhen-Hong Kong Innovation Circle","award":["SGDX2019081623021543","ITS\/092\/17","R9051","FP902"]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61836003"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"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.<\/jats:p>","DOI":"10.3390\/e23060743","type":"journal-article","created":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T14:30:25Z","timestamp":1623681025000},"page":"743","source":"Crossref","is-referenced-by-count":5,"title":["A Nonlinear Maximum Correntropy Information Filter for High-Dimensional Neural Decoding"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-4659-8955","authenticated-orcid":false,"given":"Xi","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4634-3220","authenticated-orcid":false,"given":"Shuhang","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9153-2034","authenticated-orcid":false,"given":"Xiang","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7432-9904","authenticated-orcid":false,"given":"Xiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1878-6182","authenticated-orcid":false,"given":"Yiwen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China"},{"name":"Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1016\/j.tins.2006.07.004","article-title":"Brain\u2013machine interfaces: Past, present and future","volume":"29","author":"Lebedev","year":"2006","journal-title":"Trends Neurosci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.1126\/science.1070291","article-title":"Direct Cortical Control of 3D Neuroprosthetic Devices","volume":"296","author":"Taylor","year":"2002","journal-title":"Science"},{"key":"ref_3","first-page":"20","article-title":"A brain-machine interface instructed by direct intracorticalmicrostimulation","volume":"3","author":"Lebedev","year":"2009","journal-title":"Front. 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