Subject-based discriminative sparse representation model for detection of concealed information. (May 2017)

• Record Type: Journal Article
• Authors: Akhavan, Amir; Moradi, Mohammad Hassan; Vand, Safa Rafiei
• Is Part Of: Computer methods and programs in biomedicine. Volume 143(2017)
• Page Start: 25
• Page End: 33
• Publication Date: 2017-05
• Subjects: Sparse representation classifier -- Discriminative dictionary learning -- Sparse code -- Reconstruction error -- Concealed information test; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.02.007
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A novel discriminative sparse representation-based classifier is proposed for concealed information test. The discriminative model is learned for each subject independently. This eliminates the need for EEG dataset from several subjects in model learning stage. The proposed algorithm (ISE) benefits from averaging the reconstruction error (residual error) in sparse coding stage to overcome the low signal-to-noise ratio of single sweep data. The proposed method outperforms some state-of-the-art sparsity based methods (LC-KSVD1, 2, SADL, FDDL, JEDL and SRC) in terms of precision, sensitivity and specificity. Abstract: Background and objectives: The use of machine learning approaches in concealed information test (CIT) plays a key role in the progress of this neurophysiological field. In this paper, we presented a new machine learning method for CIT in which each subject is considered independent of the others. The main goal of this study is to adapt the discriminative sparse models to be applicable for subject-based concealed information test. Methods: In order to provide sufficient discriminability between guilty and innocent subjects, we introduced a novel discriminative sparse representation model and its appropriate learning methods. For evaluation of the method forty-four subjects participated in a mock crime scenario and their EEG data were recorded. As the model input, in this study the recurrence plot features were extracted from single trial data of different stimuli. Then the extracted feature vectors were reduced using statistical dependency method. The reduced feature vector went through the proposed subject-based sparse model in which the discrimination power of sparse code and reconstruction error were applied simultaneously. Results: Experimental results showed that the proposed approach achieved better performance than other competing discriminative sparse models. The classification accuracy, sensitivity and specificity of the presented sparsity-based method were about 93%, 91% and 95% respectively. Conclusions: Using the EEG data of a single subject in response to different stimuli types and with the aid of the proposed discriminative sparse representation model, one can distinguish guilty subjects from innocent ones. Indeed, this property eliminates the necessity of several subject EEG data in model learning and decision making for a specific subject.