Subject-based discriminative sparse representation model for detection of concealed information.

Journal: Computer methods and programs in biomedicine

PMID: 28391816

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.

Authors

Amir Akhavan

Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran. Electronic address: amir.akhavan@aut.ac.ir.
Mohammad Hassan Moradi

Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran. Electronic address: mhmoradi@aut.ac.ir.
Safa Rafiei Vand

Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran. Electronic address: rafiei@aut.ac.ir.

Keywords

Algorithms Artificial Intelligence Brain Mapping Crime Electrodes Electroencephalography Guilt Healthy Volunteers Humans Learning Linear Models Machine Learning Random Allocation Reproducibility of Results Sensitivity and Specificity Signal-To-Noise Ratio Support Vector Machine Truth Disclosure

External Resources

View on PubMed Access via DOI PubMed (28391816)

Subject-based discriminative sparse representation model for detection of concealed information.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals