Implementation of Machine Learning in BCI Based Lie Detection
Author:
Affiliation:
1. California State University, Fullerton,Computer Engineering Department,Fullerton,CA,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9817098/9817144/09817162.pdf?arnumber=9817162
Reference19 articles.
1. Using EEG and fNIRS Signals as Polygraph
2. Single-trial lie detection using a combined fNIRS-polygraph system
3. Lie detection from fNIR signal and NeuroImage
4. A Study on Using Blink Parameters from EEG Data for Lie Detection
5. Formulation of a novel HRV classification model as a surrogate fraudulence detection schema
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1. Pupillometry and autonomic nervous system responses to cognitive load and false feedback: an unsupervised machine learning approach;Frontiers in Neuroscience;2024-08-30
2. LSTM Model for Brain Control Interface Based-Lie Detection;2024 IEEE First International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC);2024-02-05
3. Neural Network Applications in Polygraph Scoring—A Scoping Review;Information;2023-10-13
4. Deep Learning Applications in Brain Computer Interface Based Lie Detection;2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC);2023-03-08
5. Using Neural Network Models for BCI Based Lie Detection;2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON);2022-10-26
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