Evidence map›Paper›PMID 32399071›Full record

ArticleCognitive neurodynamics2020

An adaptive decoder design based on the receding horizon optimization in BMI system.

Hongguang Pan, Wenyu Mi, Fan Wen, Weimin Zhong

Abstract read
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In one paragraph

Article in Cognitive neurodynamics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Hongguang Pan1College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an, 710054 China.
Wenyu Mi1College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an, 710054 China.
Fan Wen1College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an, 710054 China.
Weimin Zhong2Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In a motor brain-machine interface system, since the electroencephalogram signal is changing through out the process of the arm movement, the offline trained decoder with fixed weights is often unable to convert the electroencephalogram signal accurately, resulting in poor recovery of joint motor function. In this paper, a receding horizon optimization strategy is chosen to online update the decoder weights and design an adaptive Wiener-filter-based decoder. Firstly, a classical Wiener-filter-based decoder with fixed weights is brief reviewed. Secondly, the weights in Wiener-filter-based decoder are updated by minimizing the cost function, which is composed by the sum of squared position errors in the given horizon at each sampling time. The simulation shows that the recovery effect of joint motor function and neuron activity in the BMI system with the adaptive decoder are both better than that in the BMI system with the fixed decoder.

Indexed as

Adaptive decoderBrain-machine interfaceReceding horizon optimizationWiener filter

Identifiers

PMID32399071
PMCPMC7203272

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.