Lie detection using extreme learning machine: A concealed information test based on short‐time Fourier transform and binary bat optimization using a novel fitness function. (26th November 2019)

• Record Type: Journal Article
• Authors: Dodia, Shubham; Edla, Damodar R.; Bablani, Annushree; Cheruku, Ramalingaswamy
• Is Part Of: Computational intelligence. Volume 36:Number 2(2020)
• Page Start: 637
• Page End: 658
• Publication Date: 2019-11-26
• Subjects: binary bat -- brain‐computer interface -- concealed information test -- electroencephalogram -- extreme learning machine -- short‐time Fourier transform; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12256
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Lie detection is one of the major challenges that is being faced by the forensic sciences. Identification of lie on the basis of a person's mental behavior is a tedious task. Brain‐computer interface is one such medium which provides a solution to this problem by displaying visual stimuli and recording subject's brain responses. A P300 response is elicited whenever a person comes across a familiar stimuli in a series of rare stimuli. This P300 response is used for the lie detection method. In the proposed concealed information test, acquired signals are preprocessed to discard noise. Then, short‐time Fourier transform method is applied to extract features from the preprocessed electroencephalogram signals. To avoid the curse of dimensionality and to reduce computational overhead, binary bat algorithm is applied, which helps in choosing optimal subset of features. The obtained set of features is given as an input to the extreme learning machine classifier for training of guilty and innocent samples. The performance of the system is assessed using 10‐fold cross‐validation. The resultant accuracy obtained from the proposed lie detection system is 88.3%. The system has provided efficient results in contrast with most of the state‐of‐the‐art lie detection methods.