Attached
Deception detection is important for legal, moral
and clinical purposes but still it is harder even for security officers
and judges. Therefore an effective,light weight approach is a
must.There are several technologies used in deception detection.
EEG based deception detection is one such approach. P300 wave
is most commonly used in EEG based deception detection which
depends on a stimuli. The study provides an alternative approach
to deception detection instead of using P300.Twelve subjects were
participated to the study and EEG signals were recorded while
they were telling truths and lies. The preprocessed EEG data
then fed in to feature extraction and machine learning algorithm
alone with Common Spatial Patterns (CSP) paradigm to create
a model. Logistic regression classifier was used as the machine
learning algorithm to classify the eeg signal. The test data were
used on the trained model with cross validation. There were
significant difference between truth telling and lying signals. The
average rate of correctly predicted the class was 76%.
