Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Understanding Deception

Understanding Deception

Deception is a pervasive aspect of human communication. Empirical studies have shown that most individuals engage in some form of deceit on a daily basis, with approximately 20% of social exchanges involving deceptive elements. Lying follows a developmental trajectory, peaking during adolescence and declining with age, possibly due to the maturation of cognitive control and social accountability.Cognitive and Social Factors in Deception DetectionDespite its prevalence, accurately detecting...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

<b>A new mud-nesting Fanged Frog (Anura: Dicroglossidae) from Arunachal Pradesh with notes on <i>L. longchuanensis</i> from India</b>.

Zootaxa·2026

Same author

Machine learning assisted multi-criteria decision-making approaches for site selection: A systematic review.

MethodsX·2026

Same author

Total Hip Arthroplasty for Post-Traumatic Hip Arthritis in Chronic Pubic Diastasis: A Case Report.

Case reports in orthopedics·2026

Same author

Tomato leaf disease and severity prediction using multi-task learning.

BMC plant biology·2026

Same author

The role of the gut microbiome in antibiotic-driven antimicrobial resistance.

Frontiers in microbiology·2026

Same author

Distinct and common dynamics of central brain spontaneous neuronal activity across cell types during Drosophila pupal development.

Scientific reports·2026

Search research articles

Related Experiment Video

Updated: May 11, 2026

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Multimodal machine learning for deception detection using behavioral and physiological data.

Gargi Joshi¹, Vaibhav Tasgaonkar¹, Aditya Deshpande¹

¹Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune, India.

Scientific Reports

|March 16, 2025

Summary

This study introduces CogniModal-D, a new multimodal dataset for deception detection in the Indian population. Integrating multiple data types significantly improves lie detection accuracy compared to single-source methods.

Keywords:

Affective computing Automated deception detection Behavioural data Cognitive behaviour analysis Lie detection Multimodal data fusion Neurophysiological data

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Related Experiment Videos

Last Updated: May 11, 2026

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Area of Science:

Cognitive Science
Machine Learning
Forensic Science

Background:

Deception detection is vital for national security, judiciary, and legal systems.
Traditional polygraphs face scientific and ethical challenges.
Existing datasets for deception detection are limited, small, unimodal, and non-representative of diverse populations.

Purpose of the Study:

To introduce CogniModal-D, a novel, real-world, multimodal dataset for deception detection tailored to the Indian population.
To address the limitations of existing datasets in terms of size, modality, and population diversity.
To evaluate the efficacy of multimodal AI approaches for automated deception detection.

Main Methods:

Collected data across seven modalities: electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), eye-gaze, galvanic skin response (GSR), audio, and video from over 100 Indian subjects.
Utilized tasks involving social relationships and controlled mock crime interrogations.
Developed and applied a multimodal AI-based score-level fusion technique to integrate diverse cues.

Main Results:

Multimodal fusion significantly improved deception detection accuracy by up to 15% in mock crime and best friend scenarios compared to unimodal methods.
Behavioral modalities (audio, video, gaze, GSR) demonstrated greater robustness than neurophysiological modalities (EEG, ECG, EOG).
The CogniModal-D dataset provides a valuable resource for advancing deception detection research.

Conclusions:

Multimodal features offer superior discriminatory power for deception detection.
Integrating multiple data streams is crucial for developing robust and scalable deception detection systems.
The findings support the development of advanced AI-driven lie detection technologies.