Evidence map›Paper›PMID 36320592›Full record

ArticleEURASIP journal on advances in signal processing2022

Feature stability and setup minimization for EEG-EMG-enabled monitoring systems.

Giulia Cisotto, Martina Capuzzo, Anna Valeria Guglielmi, Andrea Zanella

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In one paragraph

Article in EURASIP journal on advances in signal processing, 2022. 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. Article
  2. 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.

Giulia CisottoDepartment of Information Engineering, University of Padova, Via Gradenigo, 6, 35121 Padova, Italy.ORCID 0000-0002-9554-9367
Martina CapuzzoDepartment of Information Engineering, University of Padova, Via Gradenigo, 6, 35121 Padova, Italy.
Anna Valeria GuglielmiDepartment of Information Engineering, University of Padova, Via Gradenigo, 6, 35121 Padova, Italy.
Andrea ZanellaDepartment of Information Engineering, University of Padova, Via Gradenigo, 6, 35121 Padova, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Delivering health care at home emerged as a key advancement to reduce healthcare costs and infection risks, as during the SARS-Cov2 pandemic. In particular, in motor training applications, wearable and portable devices can be employed for movement recognition and monitoring of the associated brain signals. This is one of the contexts where it is essential to minimize the monitoring setup and the amount of data to collect, process, and share. In this paper, we address this challenge for a monitoring system that includes high-dimensional EEG and EMG data for the classification of a specific type of hand movement. We fuse EEG and EMG into the magnitude squared coherence (MSC) signal, from which we extracted features using different algorithms (one from the authors) to solve binary classification problems. Finally, we propose a

Indexed as

Consensus clusteringCyber-physical systemsData fusionEEGEMGFeature selectionFeature stabilityHuman activity recognitionMagnitude-square coherenceMobile healthMulti-modalUsability

Identifiers

PMID36320592
PMCPMC9612609

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.