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An efficient EEG based deceit identification test using wavelet packet transform and linear discriminant analysis.

Shubham Dodia1, Damodar Reddy Edla1, Annushree Bablani1

  • 1Department of Computer Science and Engineering, National Institute of Technology, Goa, India.

Journal of Neuroscience Methods
|January 21, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a brain-computer interface (BCI) for a Deceit Identification Test (DIT) using P300 signals. The novel approach achieved 91.67% accuracy in detecting deceit, offering a promising tool for EEG-based BCI applications.

Keywords:
Brain–computer interfaceDeceit identification test (DIT)ElectroencephalogramLinear discriminant analysis (LDA)Wavelet packet transform (WPT)

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Area of Science:

  • Neuroscience and Biomedical Engineering
  • Signal Processing and Machine Learning

Background:

  • Brain-computer interfaces (BCIs) offer non-muscular communication channels for individuals with neuromuscular disabilities.
  • BCIs have diverse applications, including medicine, entertainment, and lie detection.
  • This research focuses on utilizing BCI for a Deceit Identification Test (DIT) based on P300 signals.

Purpose of the Study:

  • To develop and evaluate an efficient BCI-based method for deceit identification.
  • To recognize and classify P300 signals accurately for lie detection.
  • To compare the proposed method with existing deceit identification techniques.

Main Methods:

  • EEG data were collected from 20 subjects (10 guilty, 10 innocent).
  • Pre-processing involved band-pass filtering to remove artifacts.
  • Wavelet Packet Transform (WPT) was used for feature extraction, and Linear Discriminant Analysis (LDA) served as the classifier.

Main Results:

  • The proposed WPT and LDA approach achieved an accuracy of up to 91.67%.
  • The method demonstrated high accuracy, sensitivity, and specificity in identifying deceit.
  • Performance was superior compared to other existing methods like BCD, BAD, and BPNN.

Conclusions:

  • The developed BCI-based method is highly effective for deceit identification using EEG data.
  • The approach using WPT and LDA offers significant improvements over existing techniques.
  • This efficient method holds potential for real-world applications in lie detection via BCI.