Key result
An empirical mode decomposition-based feature extraction method using EEG signals achieved a classification accuracy of 92.73% for detecting guilty and innocent subjects.
Why the study?
Does EMD-based feature extraction from multiple EEG channels improve the classification accuracy of guilty and innocent subjects in P300-based deception detection?
Population
Subjects undergoing P300-based guilty knowledge test with recorded EEG signals
Comparison
Empirical mode decomposition-based feature… vs Previously used methods
Design
Other
Authors
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Hypothesis-generating for EMD-enhanced P300 deception detection; prospective validation needed before forensic adoption.
Does EMD-based feature extraction from multiple EEG channels improve the classification accuracy of guilty and innocent subjects in P300-based deception detection?
An EMD-based feature extraction method utilizing multiple EEG channels and a genetic algorithm achieves high accuracy (92.73%) in P300-based deception detection.
Arasteh et al. (2016) studied Deception detection. Empirical mode decomposition (EMD) based feature extraction with genetic algorithm vs. Previously used methods (morphological, frequency, and wavelet features) was evaluated on Classification accuracy of guilty and innocent subjects. An empirical mode decomposition-based feature extraction method using EEG signals achieved a classification accuracy of 92.73% for detecting guilty and innocent subjects.
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