An efficient EEG based deceit identification test using wavelet packet transform and linear discriminant analysis.
Brain–computer interface
Deceit identification test (DIT)
Electroencephalogram
Linear discriminant analysis (LDA)
Wavelet packet transform (WPT)
Journal
Journal of neuroscience methods
ISSN: 1872-678X
Titre abrégé: J Neurosci Methods
Pays: Netherlands
ID NLM: 7905558
Informations de publication
Date de publication:
15 02 2019
15 02 2019
Historique:
received:
09
10
2018
revised:
26
12
2018
accepted:
15
01
2019
pubmed:
21
1
2019
medline:
18
6
2020
entrez:
21
1
2019
Statut:
ppublish
Résumé
Brain-computer interface (BCI) is a combination of hardware and software that provides a non-muscular channel to send various messages and commands to the outside world and control external devices such as computers. BCI helps severely disabled patients having neuromuscular injuries, locked-in syndrome (LiS) to lead their life as a normal person to the best extent possible. There are various applications of BCI not only in the field of medicine but also in entertainment, lie detection, gaming, etc. METHODOLOGY: In this work, using BCI a Deceit Identification Test (DIT) is performed based on P300, which has a positive peak from 300 ms to 1000 ms of stimulus onset. The goal is to recognize and classify P300 signals with excellent results. The pre-processing has been performed using the band-pass filter to eliminate the artifacts. Wavelet packet transform (WPT) is applied for feature extraction whereas linear discriminant analysis (LDA) is used as a classifier. Comparison with the other existing methods namely BCD, BAD, BPNN etc has been performed. A novel experiment is conducted using EEG acquisition device for the collection of data set on 20 subjects, where 10 subjects acted as guilty and 10 subjects acted as innocent. Training and testing data are in the ratio of 90:10 and the accuracy obtained is up to 91.67%. The proposed approach that uses WPT and LDA results in high accuracy, sensitivity, and specificity. The method provided better results in comparison with the other existing methods. It is an efficient approach for deceit identification for EEG based BCI.
Sections du résumé
BACKGROUND
Brain-computer interface (BCI) is a combination of hardware and software that provides a non-muscular channel to send various messages and commands to the outside world and control external devices such as computers. BCI helps severely disabled patients having neuromuscular injuries, locked-in syndrome (LiS) to lead their life as a normal person to the best extent possible. There are various applications of BCI not only in the field of medicine but also in entertainment, lie detection, gaming, etc. METHODOLOGY: In this work, using BCI a Deceit Identification Test (DIT) is performed based on P300, which has a positive peak from 300 ms to 1000 ms of stimulus onset. The goal is to recognize and classify P300 signals with excellent results. The pre-processing has been performed using the band-pass filter to eliminate the artifacts.
COMPARISON WITH EXISTING METHODS
Wavelet packet transform (WPT) is applied for feature extraction whereas linear discriminant analysis (LDA) is used as a classifier. Comparison with the other existing methods namely BCD, BAD, BPNN etc has been performed.
RESULTS
A novel experiment is conducted using EEG acquisition device for the collection of data set on 20 subjects, where 10 subjects acted as guilty and 10 subjects acted as innocent. Training and testing data are in the ratio of 90:10 and the accuracy obtained is up to 91.67%. The proposed approach that uses WPT and LDA results in high accuracy, sensitivity, and specificity.
CONCLUSION
The method provided better results in comparison with the other existing methods. It is an efficient approach for deceit identification for EEG based BCI.
Identifiants
pubmed: 30660481
pii: S0165-0270(19)30013-5
doi: 10.1016/j.jneumeth.2019.01.007
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
31-40Informations de copyright
Copyright © 2019 Elsevier B.V. All rights reserved.