Lie Detection Based EEG-P300 Signal Classified by ANFIS Method

Arjon Turnip, M. Faizal Amri, M. Agung Suhendra, Dwi Esti Kusumandari


In this paper, the differences in brain signal activity (EEG-P300 component) which detects whether a person is telling the truth or lying is explored. Brain signal activity is monitored when they are first respond to a given experiment stimulus. In the experiment, twelve subjects whose age are around 19 ± 1 years old were involved. In the signal processing, the recorded brain signals were filtered and extracted using bandpass filter and independent component analysis, respectively. Furthermore, the extracted signals were classified with adaptive neuro-fuzzy inference system method. The results show that a huge spike of the EEG-P300 amplitude on a lying subject correspond to the appeared stimuli is achieved. The findings of these experiments have been promising in testing the validity of using an EEG-P300 as a lie detector.


Lie Detection; ERP; EEG-P300; ANFIS; Feature Extraction; Classification;

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