Feature stability and setup minimization for EEG-EMG-enabled monitoring systems.
Consensus clustering
Cyber-physical systems
Data fusion
EEG
EMG
Feature selection
Feature stability
Human activity recognition
Magnitude-square coherence
Mobile health
Multi-modal
Usability
Journal
EURASIP journal on advances in signal processing
ISSN: 1687-6172
Titre abrégé: EURASIP J Adv Signal Process
Pays: United States
ID NLM: 101480232
Informations de publication
Date de publication:
2022
2022
Historique:
received:
04
10
2021
accepted:
18
10
2022
entrez:
2
11
2022
pubmed:
3
11
2022
medline:
3
11
2022
Statut:
ppublish
Résumé
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
Identifiants
pubmed: 36320592
doi: 10.1186/s13634-022-00939-3
pii: 939
pmc: PMC9612609
doi:
Types de publication
Journal Article
Langues
eng
Pagination
103Informations de copyright
© The Author(s) 2022.
Déclaration de conflit d'intérêts
Competing interestsThe authors declare that they have no competing interests.
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