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

Akhavan, Amir Naser; Moradi, Mohammad Hassan; Vand, Safa Rafiei

doi:10.1016/j.cmpb.2017.02.007

Cited by 16 publications

(4 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are many studies that analyzed the CIT classification improvements using different ML algorithms. Even though datasets with EDA parameters such as latency, amplitude, slope, and rise time, were not used elsewhere, the results of this study are in compliance with other papers confirming that ML algorithms may be used successfully to improve the CIT classification results (Farahani and Moradi 2013, Gao et al 2013, Farahani and Moradi 2017, Akhavan et al 2017, Rosenfeld 2018, Matsuda et al 2019, Derakhshan et al 2020.…”

Section: Discussionsupporting

confidence: 84%

Interactive slide selection algorithm and machine learning in psychophysiological memory testing

Randjelovic¹

2023

Physiol. Meas.

View full text Add to dashboard Cite

Objective: To present the new type of concealed information test (CIT) that implements the interactive slide selection (ISS) algorithm and compare its effectiveness with the standard CIT (sCIT). Approach: The ISS algorithm presents slides interactively, based on the analysis of electrodermal activity (EDA), while sCIT presents slides in a predefined, sequential order. The algorithm automatically selects irrelevant, relevant, and control slides and presents them at the moment which is physiologically most suitable for electrodermal response (EDR) detection. To compare the ISS-based CIT (issCIT) and sCIT, two objects, a bag, and a wallet, were presented to 64 participants, 32 of which were analyzed with sCIT, and another 32 with issCIT. Main results: The results show that ISS had significantly better true/false predictions (Fisher’s exact test, p<0.01). Also, the number of false positives (FPs) was significantly lower in the issCIT group in comparison with sCIT (Fisher’s exact test, p<0.001). Machine learning (ML) classifiers improved precision from 49% to 79% in the sCIT group (McNemar’s test, p<0.05), and from 85% to 100% in the issCIT group (McNemar’s test, p<0.05). The testing time in the issCIT group ranged between 42-107s, while the average was 53s. In the sCIT group, the testing time was always 330s. Significance: Under the presented experimental settings, the ISS algorithm obtained significantly better classification results compared to sCIT, while the application of the ML algorithms managed to improve the classification results in both groups reaching the precision of 100%. The ISS algorithm allowed for a much shorter testing time compared to sCIT.

show abstract

Section: Discussionsupporting

confidence: 84%

Interactive slide selection algorithm and machine learning in psychophysiological memory testing

Randjelovic¹

2023

Physiol. Meas.

View full text Add to dashboard Cite

show abstract

“…Nonparametric-based feature extraction with LDA and KNN as classifier has been used. A spare representation method has been used on irrelevant and target ERP responses for single subject [11]. In [12], genetic SVM as classifier has been applied to identify guilty subject using a novel CIT method.…”

Section: Machine Learning Approachmentioning

confidence: 99%

“…EEG is a signal waveform, and its amplitude provides the value of a signal at each peak; power provides the strength of the signal; the frequency component of a signal is extracted using FFT and statistical parameter using Hjorth features (mobility and complexity); and wavelet is used to extract time-frequency components of EEG signal. Hence, instead of using a single type of feature extraction technique like [8,11], we have used various feature extraction techniques to analyze EEG data more precisely. After signal preprocessing, each feature extraction approach (as discussed in Sect.…”

Section: Feature Extractionmentioning

confidence: 99%

An efficient Concealed Information Test: EEG feature extraction and ensemble classification for lie identification

Bablani

Edla

Tripathi³

et al. 2018

Machine Vision and Applications

View full text Add to dashboard Cite

EEG-based lie detectors have become popular over polygraphs because it cannot be controlled by human intentions. Various studies have performed "Guilty Knowledge Test" or "Concealed Information Test" by creating a mock crime scenario to identify changes in brain potential. In this study, an individual's behavior during lying is analyzed and a new scenario is developed for "Concealed Information Test." This work involves a mock crime scenario using an EEG acquisition device for 10 participants. Data acquisition has been performed by placing 16 electrodes on the subjects' scalp. For this experiment, the subject has to recognize faces of some known and unknown personalities among 10 images flashed. These images behave as stimulus for the subject which generate corresponding brain responses. Various feature extraction approaches such as statistical, time domain, frequency domain and time-frequency domain are applied to the 16-channel EEG data. For classifying a subject as guilty or innocent, five classifiers have been applied on subject-wise EEG data. Moreover, the classifiers' ranking is considered based on the performance of classifiers. An ensemble framework is developed by aggregating the results of the best three classifiers out of the tested five classifiers. The classifiers' results are aggregated using a weighted voting approach and have been compared with popular conventional approaches using various classification performance measures. Results present a comparative performance of different feature extraction approaches and classifiers using subjectwise single-trial EEG data. The wavelet approach performs better for EEG data of most of the subjects. A comparison between base classifiers and ensemble framework is provided with the ensemble approach outperforming over the base classifiers. Further proposed framework is compared with some existing approaches, and a highest accuracy of 92.4% has been achieved.

show abstract

“…In another approach, sparsity-based classification models have been applied to CIT data. 11 The performance of discriminating property of sparse model has been improved by developing a single framework for reconstruction error and sparse code features. Instead of analyzing all subject's data together, subject-based classification has been performed and received better performance for individual analysis.…”

Section: Introductionmentioning

confidence: 99%

A multi stage EEG data classification using k-means and feed forward neural network

Bablani

Edla

Kuppili

et al. 2020

Clinical Epidemiology and Global Health

View full text Add to dashboard Cite

Electroencephalography based brain-computer interface techniques are widely used these days as they bring human intentions into reality. Various researchers have proposed different approaches to decode EEG signals for applications like spellers, emotion recognition, lie detection, brain games etc. The ability to analyze concealed behavior is very important for legal and security purposes. Method: This research aims to identify the concealed behavior of an individual. This paper presents a three-stage "Concealed Information Test" using wavelet transform, k-means clustering and multi-layer feed-forward neural network. The test has been developed by analyzing ERP component (P300) of EEG data during a mock crime scene. The wavelet transforms extracts time and frequency information from raw EEG data. K-means clustering clusters the wavelet coefficients into three clusters. As, neural network models the nonlinear time series data viz EEG, hence it has been utilized for classification of clustered data. Results: A hybrid 3-stage classification approach is proposed by combining the advantages of all above-mentioned approaches. EEG data is recorded for a "Concealed Information Test", for implementing the proposed framework. Conclusion: The performance of the proposed system is improved from existing approaches, by providing an accuracy of 83.1%.

show abstract

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

Cited by 16 publications

References 40 publications

Interactive slide selection algorithm and machine learning in psychophysiological memory testing

Interactive slide selection algorithm and machine learning in psychophysiological memory testing

An efficient Concealed Information Test: EEG feature extraction and ensemble classification for lie identification

A multi stage EEG data classification using k-means and feed forward neural network

Contact Info

Product

Resources

About