Truth Identification from EEG Signal by using Convolution neural network: Lie Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F20%3APU136960" target="_blank" >RIV/00216305:26220/20:PU136960 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/TSP49548.2020.9163497" target="_blank" >http://dx.doi.org/10.1109/TSP49548.2020.9163497</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TSP49548.2020.9163497" target="_blank" >10.1109/TSP49548.2020.9163497</a>
Alternative languages
Result language
angličtina
Original language name
Truth Identification from EEG Signal by using Convolution neural network: Lie Detection
Original language description
Identification of statement is truth or lie is a major problem. It has various applications for safety and clime control. Traditionally physiological activities are monitored during the question-answer round and compare to a normal level. However, because the subject can control his/her physiological reactions, therefore, to overcome these brain signals are used to identify the truth. Brain signal is the first to respond to any sensory impulses which can be used to identify the person is telling the truth or lying. The EEG signals describe the brain signal activity of a person. In this paper, a deep learning method has been used for automatic truth identification from EEG signals by using a convolution neural network. The proposed model has taken 14 channel EEG signals as input to convolution neural network for classification of the signal into the truth or lies statements. The proposed method has achieved up to 84.44% accuracy to identify a person is telling a truth or lie. The proposed method is non-invasive, efficient and robust and has low time complexity making it suitable for realtime applications.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2020 43rd International Conference on Telecommunications and Signal Processing (TSP)
ISBN
978-1-7281-6376-5
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
550-553
Publisher name
IEEE
Place of publication
Milan, Italy
Event location
Milan, Italy
Event date
Jul 7, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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