title: Subject-based discriminative sparse representation model for detection of concealed information.
abstract: OBJECTIVE
  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.
authors.affiliation: Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran. Electronic address: amir.akhavan@aut.ac.ir.
authors.forename: Amir
authors.initials: A
authors.lastname: Akhavan
authors.affiliation: Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran. Electronic address: mhmoradi@aut.ac.ir.
authors.forename: Mohammad Hassan
authors.initials: MH
authors.lastname: Moradi
authors.affiliation: Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran. Electronic address: rafiei@aut.ac.ir.
authors.forename: Safa Rafiei
authors.initials: SR
authors.lastname: Vand
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001931:Brain Mapping; D003415:Crime; D004566:Electrodes; D004569:Electroencephalography; D006167:Guilt; D064368:Healthy Volunteers; D006801:Humans; D007858:Learning; D016014:Linear Models; D000069550:Machine Learning; D011897:Random Allocation; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D059629:Signal-To-Noise Ratio; D060388:Support Vector Machine; D014342:Truth Disclosure
keywords: Concealed information test; Discriminative dictionary learning; Reconstruction error; Sparse code; Sparse representation classifier