title: A new approach to analyze data from EEG-based concealed face recognition system.
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.
authors.affiliation: Department of Biomedical Engineering, Faculty of Engineering, Shahed University, P.O.Box: 3319118651, Tehran, Iran.
authors.forename: A H
authors.initials: AH
authors.lastname: Mehrnam
authors.affiliation: Department of Biomedical Engineering, Faculty of Engineering, Shahed University, P.O.Box: 3319118651, Tehran, Iran. Electronic address: Nasrabadi@shahed.ac.ir.
authors.forename: A M
authors.initials: AM
authors.lastname: Nasrabadi
authors.affiliation: Department of Biomedical Engineering, Faculty of Engineering, Shahed University, P.O.Box: 3319118651, Tehran, Iran.
authors.forename: Mahrad
authors.initials: M
authors.lastname: Ghodousi
authors.affiliation: 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.
authors.forename: A
authors.initials: A
authors.lastname: Mohammadian
authors.affiliation: Research Center of Intelligent Signal Processing, P.O.Box: 16765-3739, Tehran, Iran.
authors.forename: Sh
authors.initials: S
authors.lastname: Torabi
mesh_terms: D000293:Adolescent; D000328:Adult; D002540:Cerebral Cortex / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D000066499:Facial Recognition / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D055815:Young Adult
keywords: Concealed face recognition test; Non-linear features; Recurrence Quantification Analysis; Single-trial ERP