Soft boundary-based neurofeedback training based on fuzzy similarity measures: A method for learning how to control EEG Signal features during neurofeedback training.
EEG
Fuzzy similarity
Scoring index
Soft boundary-based neurofeedback training
Threshold
Journal
Journal of neuroscience methods
ISSN: 1872-678X
Titre abrégé: J Neurosci Methods
Pays: Netherlands
ID NLM: 7905558
Informations de publication
Date de publication:
01 09 2020
01 09 2020
Historique:
received:
30
10
2019
revised:
05
06
2020
accepted:
08
06
2020
pubmed:
17
6
2020
medline:
22
6
2021
entrez:
17
6
2020
Statut:
ppublish
Résumé
Most commonly used neurofeedback training (NFT) methods are able to assist subjects towards an increase/decrease in EEG features. So, it is possible that the enhancement/inhabitation in a subject's EEG features exceed normal limits if the process of changes in brain activity in the subject is very successful. This issue may also bring about a reduction in the effectiveness of NFT. A soft boundary-based NFT method was proposed for learning how to control the EEG features during training. According to this method, an initial group was defined within which the training features of subjects' EEG signals were placed prior to training and a target group was considered referring to what the features of the EEG signals should be shifted towards during training. In the course of training, the fuzzy similarity of EEG features of subject towards the target group center was measured and the subject's score was increased if their fuzzy similarity was higher than a threshold. Within this method, an adaptive scoring index (the scores assigned to subjects for each achievement) was defined whose value was determined according to brain activity of the subject. Increase/decrease in large amounts in the training features of subject's EEG could lead to a descending trend in the scores received using the proposed method. The proposed method may assist subjects to control their EEG signal features within the target group range. The proposed method may be able to prevent the side effects of neurofeedback.
Sections du résumé
BACKGROUND
Most commonly used neurofeedback training (NFT) methods are able to assist subjects towards an increase/decrease in EEG features. So, it is possible that the enhancement/inhabitation in a subject's EEG features exceed normal limits if the process of changes in brain activity in the subject is very successful. This issue may also bring about a reduction in the effectiveness of NFT.
NEW METHOD
A soft boundary-based NFT method was proposed for learning how to control the EEG features during training. According to this method, an initial group was defined within which the training features of subjects' EEG signals were placed prior to training and a target group was considered referring to what the features of the EEG signals should be shifted towards during training. In the course of training, the fuzzy similarity of EEG features of subject towards the target group center was measured and the subject's score was increased if their fuzzy similarity was higher than a threshold. Within this method, an adaptive scoring index (the scores assigned to subjects for each achievement) was defined whose value was determined according to brain activity of the subject.
RESULTS
Increase/decrease in large amounts in the training features of subject's EEG could lead to a descending trend in the scores received using the proposed method.
COMPARISON WITH EXISTING METHODS
The proposed method may assist subjects to control their EEG signal features within the target group range.
CONCLUSION
The proposed method may be able to prevent the side effects of neurofeedback.
Identifiants
pubmed: 32544535
pii: S0165-0270(20)30228-4
doi: 10.1016/j.jneumeth.2020.108805
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
108805Informations de copyright
Copyright © 2020. Published by Elsevier B.V.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare no conflicts of interest with respect to the research, authorship, and/or publication of this article.