Distraction descriptor for brainprint authentication modelling using probability-based Incremental Fuzzy-Rough Nearest Neighbour.
Brainprint authentication
Distraction descriptor
Object variation
Probability-based IncFRNN
Journal
Brain informatics
ISSN: 2198-4018
Titre abrégé: Brain Inform
Pays: Germany
ID NLM: 101673751
Informations de publication
Date de publication:
05 Aug 2023
05 Aug 2023
Historique:
received:
20
02
2023
accepted:
07
07
2023
medline:
6
8
2023
pubmed:
6
8
2023
entrez:
5
8
2023
Statut:
epublish
Résumé
This paper aims to design distraction descriptor, elicited through the object variation, to refine the granular knowledge incrementally, using the proposed probability-based incremental update strategy in Incremental Fuzzy-Rough Nearest Neighbour (IncFRNN) technique. Most of the brainprint authentication models were tested in well-controlled environments to minimize the influence of ambient disturbance on the EEG signals. These settings significantly contradict the real-world situations. Thus, making use of the distraction is wiser than eliminating it. The proposed probability-based incremental update strategy is benchmarked with the ground truth (actual class) incremental update strategy. Besides, the proposed technique is also benchmarked with First-In-First-Out (FIFO) incremental update strategy in K-Nearest Neighbour (KNN). The experimental results have shown equivalence discriminatory performance in both high distraction and quiet conditions. This has proven that the proposed distraction descriptor is able to utilize the unique EEG response towards ambient distraction to complement person authentication modelling in uncontrolled environment. The proposed probability-based IncFRNN technique has significantly outperformed the KNN technique for both with and without defining the window size threshold. Nevertheless, its performance is slightly worse than the actual class incremental update strategy since the ground truth represents the gold standard. In overall, this study demonstrated a more practical brainprint authentication model with the proposed distraction descriptor and the probability-based incremental update strategy. However, the EEG distraction descriptor may vary due to intersession variability. Future research may focus on the intersession variability to enhance the robustness of the brainprint authentication model.
Identifiants
pubmed: 37542531
doi: 10.1186/s40708-023-00200-z
pii: 10.1186/s40708-023-00200-z
pmc: PMC10404212
doi:
Types de publication
Journal Article
Langues
eng
Pagination
21Informations de copyright
© 2023. Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Sensors (Basel). 2013 Aug 09;13(8):10273-86
pubmed: 23939584
Neurobiol Aging. 2009 Jul;30(7):1157-72
pubmed: 18023507
IEEE Trans Pattern Anal Mach Intell. 2005 Apr;27(4):603-618
pubmed: 15794164
Neuroimage. 2014 May 15;92:46-55
pubmed: 24508648
Am J Electroneurodiagnostic Technol. 2006 Sep;46(3):254-74
pubmed: 17042161
J Neurosci Methods. 2007 Oct 15;166(1):41-52
pubmed: 17698205
Biol Psychol. 2002 Oct;61(1-2):111-38
pubmed: 12385672
Int J Psychophysiol. 2013 Sep;89(3):334-41
pubmed: 23688673
IEEE J Biomed Health Inform. 2015 Jan;19(1):6-21
pubmed: 25486653
Brain Res Brain Res Rev. 1999 Apr;29(2-3):169-95
pubmed: 10209231
Int J Psychophysiol. 2012 Jul;85(1):125-8
pubmed: 22155528
Neuroimage. 2018 Dec;183:897-906
pubmed: 30176369
Front Comput Neurosci. 2016 Aug 19;10:85
pubmed: 27594831
Biol Cybern. 1998 Jul;79(1):49-57
pubmed: 9742677
Brain Res Bull. 2011 Aug 10;86(1-2):110-7
pubmed: 21756985
Comput Intell Neurosci. 2021 Dec 24;2021:5229576
pubmed: 34976039