Evidence map›Paper›PMID 39555257›Full record

ArticleCognitive neurodynamics2024

Motor imagery decoding using source optimized transfer learning based on multi-loss fusion CNN.

Jun Ma, Banghua Yang, Fenqi Rong, Shouwei Gao, Wen Wang

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Article in Cognitive neurodynamics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
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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

5 authors.

Jun MaSchool of Mechatronic Engineering and Automation, School of Medicine, Research Center of Brain-Computer Engineering, Shanghai University, Shanghai, 200444 China.
Banghua YangSchool of Mechatronic Engineering and Automation, School of Medicine, Research Center of Brain-Computer Engineering, Shanghai University, Shanghai, 200444 China.
Fenqi RongSchool of Mechatronic Engineering and Automation, School of Medicine, Research Center of Brain-Computer Engineering, Shanghai University, Shanghai, 200444 China.
Shouwei GaoSchool of Mechatronic Engineering and Automation, School of Medicine, Research Center of Brain-Computer Engineering, Shanghai University, Shanghai, 200444 China.
Wen WangDepartment of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, 710038 Shaanxi China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transfer learning is increasingly used to decode multi-class motor imagery tasks. Previous transfer learning ignored the optimizability of the source model, weakened the adaptability to the target domain and limited the performance. This paper first proposes the multi-loss fusion convolutional neural network (MF-CNN) to make an optimizable source model. Then we propose a novel source optimized transfer learning (SOTL), which optimizes the source model to make it more in line with the target domain's features to improve the target model's performance. We transfer the model trained from 16 healthy subjects to 16 stroke patients. The average classification accuracy achieves 51.2 ± 0.17% in the four types of unilateral upper limb motor imagery tasks, which is significantly higher than the classification accuracy of deep learning (

Indexed as

Motor imageryMulti-loss fusion convolutional neural networkSource optimized transfer learningStroke rehabilitation

Identifiers

PMID39555257
PMCPMC11564427

What OpenQuestion holds

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Registered trials

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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.