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Int J Psychophysiol. 2017 Dec;122:17-23. doi: 10.1016/j.ijpsycho.2017.05.006. Epub 2017 May 19.

Reprint of "A new approach to analyze data from EEG-based concealed face recognition system".

Author information

1
Department of Biomedical Engineering, Faculty of Engineering, Shahed University, P.O.Box: 3319118651, Tehran, Iran.
2
Department of Biomedical Engineering, Faculty of Engineering, Shahed University, P.O.Box: 3319118651, Tehran, Iran. Electronic address: Nasrabadi@shahed.ac.ir.
3
Department of Biomedical Engineering, Faculty of Engineering, Amirkabir University of Technology, P.O.Box: 4413-15875, Tehran, Iran; Research Center of Intelligent Signal Processing, P.O.Box: 16765-3739, Tehran, Iran.
4
Research Center of Intelligent Signal Processing, P.O.Box: 16765-3739, Tehran, Iran.

Abstract

The purpose of this study is to extend a feature set with non-linear features to improve classification rate of guilty and innocent subjects. Non-linear features can provide extra information about phase space. The Event-Related Potential (ERP) signals were recorded from 49 subjects who participated in concealed face recognition test. For feature extraction, at first, several morphological characteristics, frequency bands, and wavelet coefficients (we call them basic-features) are extracted from each single-trial ERP. Recurrence Quantification Analysis (RQA) measures are then computed as non-linear features from each single-trial. We apply Genetic Algorithm (GA) to select the best feature set and this feature set is used for classification of data using Linear Discriminant Analysis (LDA) classifier. Next, we use a new approach to improve classification results based on introducing an adaptive-threshold. Results indicate that our method is able to correctly detect 91.83% of subjects (45 correct detection of 49 subjects) using combination of basic and non-linear features, that is higher than 87.75% for basic and 79.59% for non-linear features. This shows that combination of non-linear and basic- features could improve classification rate.

KEYWORDS:

Concealed face recognition test; Non-linear features; Recurrence Quantification Analysis; Single-trial ERP

PMID:
28532643
DOI:
10.1016/j.ijpsycho.2017.05.006
[Indexed for MEDLINE]

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