Truth Identification from EEG Signal by using Convolution neural network: Lie Detection

conference paper

English

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.

EEG Signal,truth identification,deep neural network,lie detection

BAGHEL, N., SINGH, D, DUTTA, M.K., BURGET, R., & MYSKA, V. Truth Identification from EEG Signal by using Convolution neural network: Lie Detection

11. 8. 2020

IEEE

Milan, Italy

978-1-7281-6376-5

2020 43rd International Conference on Telecommunications and Signal Processing (TSP)

550

553

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