Evidence map›Paper›PMID 36679501›Full record

ArticleSensors (Basel, Switzerland)2023

Robust Motor Imagery Tasks Classification Approach Using Bayesian Neural Network.

Daily Milanés-Hermosilla, Rafael Trujillo-Codorniú, Saddid Lamar-Carbonell, Roberto Sagaró-Zamora, Jorge Jadid Tamayo-Pacheco, John Jairo Villarejo-Mayor, Denis Delisle-Rodriguez

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Daily Milanés-HermosillaDepartment of Automatic Engineering, University of Oriente, Santiago de Cuba 90500, Cuba.ORCID 0000-0003-4463-9263
Rafael Trujillo-CodorniúDepartment of Automatic Engineering, University of Oriente, Santiago de Cuba 90500, Cuba.ORCID 0000-0001-7449-1939
Saddid Lamar-CarbonellIndependent Researcher, 33720 Tampere, Finland.
Roberto Sagaró-ZamoraDepartment of Mechanical Engineering, University of Oriente, Santiago de Cuba 90500, Cuba.ORCID 0000-0001-5808-1999
Jorge Jadid Tamayo-PachecoIndependent Researcher, 33720 Tampere, Finland.
John Jairo Villarejo-MayorDepartment of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianopolis 88040-900, SC, Brazil.ORCID 0000-0003-2325-1518
Denis Delisle-RodriguezPostgraduate Program in Neuroengineering, Edmond and Lily Safra International Institute of Neurosciences, Santos Dumont Institute, Macaiba 59280-000, RN, Brazil.ORCID 0000-0002-8937-031X

Funding

National Science Project "Development of upper limb exo-503 skeletons for rehabilitation task" PN223LH004-026
6 · The paper itself

Abstract

The development of Brain-Computer Interfaces based on Motor Imagery (MI) tasks is a relevant research topic worldwide. The design of accurate and reliable BCI systems remains a challenge, mainly in terms of increasing performance and usability. Classifiers based on Bayesian Neural Networks are proposed in this work by using the variational inference, aiming to analyze the uncertainty during the MI prediction. An adaptive threshold scheme is proposed here for MI classification with a reject option, and its performance on both datasets 2a and 2b from BCI Competition IV is compared with other approaches based on thresholds. The results using subject-specific and non-subject-specific training strategies are encouraging. From the uncertainty analysis, considerations for reducing computational cost are proposed for future work.

Indexed as

Brain-Computer InterfacesElectroencephalographyAlgorithmsBayes TheoremImagery, PsychotherapyImaginationNeural Networks, ComputerBayesian Neural Networkbrain–computer interfaceclassification with reject optionuncertainty estimationvariational inference

Identifiers

PMID36679501
PMCPMC9862912

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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.